{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Case Study"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below mentioned are the steps to be followed:\n",
    "\n",
    "<b>Part 1</b>\n",
    "1. Load the data\n",
    "2. Understand and Viz the data\n",
    "    - Missing value treatment\n",
    "    - EDA\n",
    "3. Data Preparation\n",
    "    - Removing Outliers and uneccessary columns\n",
    "    - Categorical variable treatment\n",
    "    - Standardizing numerical variables\n",
    "    - Train Test Split\n",
    "4. Training the Model\n",
    "5. Prediction\n",
    "    - Making Predictions\n",
    "6. Evaluation\n",
    "\n",
    "<b>Part 2</b> Automatic Feature Elimination using RFE\n",
    "\n",
    "<b>Part 3</b> Principal Component Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the required libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Suppressing Warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"adult.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Understand and Viz the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>226802</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>89814</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>?</td>\n",
       "      <td>103497</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  workclass  fnlwgt     education  educational-num      marital-status  \\\n",
       "0   25    Private  226802          11th                7       Never-married   \n",
       "1   38    Private   89814       HS-grad                9  Married-civ-spouse   \n",
       "2   28  Local-gov  336951    Assoc-acdm               12  Married-civ-spouse   \n",
       "3   44    Private  160323  Some-college               10  Married-civ-spouse   \n",
       "4   18          ?  103497  Some-college               10       Never-married   \n",
       "\n",
       "          occupation relationship   race  gender  capital-gain  capital-loss  \\\n",
       "0  Machine-op-inspct    Own-child  Black    Male             0             0   \n",
       "1    Farming-fishing      Husband  White    Male             0             0   \n",
       "2    Protective-serv      Husband  White    Male             0             0   \n",
       "3  Machine-op-inspct      Husband  Black    Male          7688             0   \n",
       "4                  ?    Own-child  White  Female             0             0   \n",
       "\n",
       "   hours-per-week native-country income  \n",
       "0              40  United-States  <=50K  \n",
       "1              50  United-States  <=50K  \n",
       "2              40  United-States   >50K  \n",
       "3              40  United-States   >50K  \n",
       "4              30  United-States  <=50K  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at the dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48842, 15)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dimensionality check\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 48842 entries, 0 to 48841\n",
      "Data columns (total 15 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   age              48842 non-null  int64 \n",
      " 1   workclass        48842 non-null  object\n",
      " 2   fnlwgt           48842 non-null  int64 \n",
      " 3   education        48842 non-null  object\n",
      " 4   educational-num  48842 non-null  int64 \n",
      " 5   marital-status   48842 non-null  object\n",
      " 6   occupation       48842 non-null  object\n",
      " 7   relationship     48842 non-null  object\n",
      " 8   race             48842 non-null  object\n",
      " 9   gender           48842 non-null  object\n",
      " 10  capital-gain     48842 non-null  int64 \n",
      " 11  capital-loss     48842 non-null  int64 \n",
      " 12  hours-per-week   48842 non-null  int64 \n",
      " 13  native-country   48842 non-null  object\n",
      " 14  income           48842 non-null  object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 5.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# Column data types\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>48842.000000</td>\n",
       "      <td>4.884200e+04</td>\n",
       "      <td>48842.000000</td>\n",
       "      <td>48842.000000</td>\n",
       "      <td>48842.000000</td>\n",
       "      <td>48842.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.643585</td>\n",
       "      <td>1.896641e+05</td>\n",
       "      <td>10.078089</td>\n",
       "      <td>1079.067626</td>\n",
       "      <td>87.502314</td>\n",
       "      <td>40.422382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.710510</td>\n",
       "      <td>1.056040e+05</td>\n",
       "      <td>2.570973</td>\n",
       "      <td>7452.019058</td>\n",
       "      <td>403.004552</td>\n",
       "      <td>12.391444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.228500e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.175505e+05</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.781445e+05</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.376420e+05</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.490400e+06</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        fnlwgt  educational-num  capital-gain  \\\n",
       "count  48842.000000  4.884200e+04     48842.000000  48842.000000   \n",
       "mean      38.643585  1.896641e+05        10.078089   1079.067626   \n",
       "std       13.710510  1.056040e+05         2.570973   7452.019058   \n",
       "min       17.000000  1.228500e+04         1.000000      0.000000   \n",
       "25%       28.000000  1.175505e+05         9.000000      0.000000   \n",
       "50%       37.000000  1.781445e+05        10.000000      0.000000   \n",
       "75%       48.000000  2.376420e+05        12.000000      0.000000   \n",
       "max       90.000000  1.490400e+06        16.000000  99999.000000   \n",
       "\n",
       "       capital-loss  hours-per-week  \n",
       "count  48842.000000    48842.000000  \n",
       "mean      87.502314       40.422382  \n",
       "std      403.004552       12.391444  \n",
       "min        0.000000        1.000000  \n",
       "25%        0.000000       40.000000  \n",
       "50%        0.000000       40.000000  \n",
       "75%        0.000000       45.000000  \n",
       "max     4356.000000       99.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistical analysis\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Observations :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                   0\n",
       "workclass          2799\n",
       "fnlwgt                0\n",
       "education             0\n",
       "educational-num       0\n",
       "marital-status        0\n",
       "occupation         2809\n",
       "relationship          0\n",
       "race                  0\n",
       "gender                0\n",
       "capital-gain          0\n",
       "capital-loss          0\n",
       "hours-per-week        0\n",
       "native-country      857\n",
       "income                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isin(['?']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                0\n",
       "workclass          0\n",
       "fnlwgt             0\n",
       "education          0\n",
       "educational-num    0\n",
       "marital-status     0\n",
       "occupation         0\n",
       "relationship       0\n",
       "race               0\n",
       "gender             0\n",
       "capital-gain       0\n",
       "capital-loss       0\n",
       "hours-per-week     0\n",
       "native-country     0\n",
       "income             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.replace('?', np.nan, inplace=True)\n",
    "df.isin(['?']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                   0\n",
       "workclass          2799\n",
       "fnlwgt                0\n",
       "education             0\n",
       "educational-num       0\n",
       "marital-status        0\n",
       "occupation         2809\n",
       "relationship          0\n",
       "race                  0\n",
       "gender                0\n",
       "capital-gain          0\n",
       "capital-loss          0\n",
       "hours-per-week        0\n",
       "native-country      857\n",
       "income                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                0.00\n",
       "workclass          5.73\n",
       "fnlwgt             0.00\n",
       "education          0.00\n",
       "educational-num    0.00\n",
       "marital-status     0.00\n",
       "occupation         5.75\n",
       "relationship       0.00\n",
       "race               0.00\n",
       "gender             0.00\n",
       "capital-gain       0.00\n",
       "capital-loss       0.00\n",
       "hours-per-week     0.00\n",
       "native-country     1.75\n",
       "income             0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentage of missing values.\n",
    "round(df.isnull().sum() * 100 / len(df.index), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                0.00\n",
       "workclass          5.74\n",
       "fnlwgt             0.00\n",
       "education          0.00\n",
       "educational-num    0.00\n",
       "marital-status     0.00\n",
       "occupation         5.76\n",
       "relationship       0.00\n",
       "race               0.00\n",
       "gender             0.00\n",
       "capital-gain       0.00\n",
       "capital-loss       0.00\n",
       "hours-per-week     0.00\n",
       "native-country     0.00\n",
       "income             0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.loc[df['native-country'].notnull(), :]\n",
    "round(df.isnull().sum() * 100 / len(df.index), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                0.0\n",
       "workclass          0.0\n",
       "fnlwgt             0.0\n",
       "education          0.0\n",
       "educational-num    0.0\n",
       "marital-status     0.0\n",
       "occupation         0.0\n",
       "relationship       0.0\n",
       "race               0.0\n",
       "gender             0.0\n",
       "capital-gain       0.0\n",
       "capital-loss       0.0\n",
       "hours-per-week     0.0\n",
       "native-country     0.0\n",
       "income             0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.loc[df['occupation'].notnull(), :]\n",
    "round(df.isnull().sum() * 100 / len(df.index), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45222, 15)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.411653904426518"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentage of rows lost in data cleaning.\n",
    "\n",
    "(48842 - len(df.index)) * 100 / 48842"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>So, we have lost 7.41% of the rows from the dataframe. This is fine considering that we have more than 45,000 datapoints in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['age', 'fnlwgt', 'educational-num', 'capital-gain', 'capital-loss',\n",
      "       'hours-per-week'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "num_attributes = df.select_dtypes(include=['int64'])\n",
    "print(num_attributes.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'age'}>,\n",
       "        <AxesSubplot:title={'center':'fnlwgt'}>],\n",
       "       [<AxesSubplot:title={'center':'educational-num'}>,\n",
       "        <AxesSubplot:title={'center':'capital-gain'}>],\n",
       "       [<AxesSubplot:title={'center':'capital-loss'}>,\n",
       "        <AxesSubplot:title={'center':'hours-per-week'}>]], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DM0tH3czMejO62QGYmRVcW0RsBYiIrZIOTeUTgPty23WlslfScnl5qc7mtK8eSc8DhwBP519Q0nyyI3O0tbXR0dHRZ4Dd3d10dHSwYGrPgBtXT21j6Dem/tpWa6X3qkiKGBMUM66RGtOQk7V02H818EREvF/SwcBNwGRgE/DhiNietl1EdhpgJ/CXEfFfqfx4stMPY4DbgfPTN1Izs6KqdEQs+ijvq87uBRFLgaUA06ZNi/b29j4D6ejooL29nbMX3tbndo22YGoPF63p+9/MpjPaGxNMUnqviqSIMUEx4xqpMdXiNOj5wPrc81qO7zAza7Yn06lN0s9tqbwLmJTbbiKwJZVPrFC+Wx1Jo4ED2PO0q5nZboaUrEmaCJwMfDVXXMvxHWZmzbYCmJuW5wK35srnpBmeh5N90bw/nTLdIWlGGo92Vlmd0r5OBe72WQQz689QT4N+Afg4sH+urJbjO3Yz0HEcJdWM46hmbEWrKXqbaz0GoIhjHepppLW3FiTdALQD4yR1AZ8ClgDLJc0DHgdOA4iItZKWA+uAHuDciNiZdnUOu4Z23JEeAFcC10rqJDuiNqcBzTKzYW7QyZqk9wPbIuIBSe3VVKlQ1t/4jt0LBziOo6SacRzVjK1oNUVvc63HshRxrEM9jbT21kJEnN7Lqpm9bL8YWFyhfDVwTIXyl0jJnplZtYbyn/oE4IOSTgJeDYyV9HXS+I50VG2o4zvMzMzMRrRBj1mLiEURMTEiJpMdyr87Ij5Cbcd3mJmZmY1o9TgHVsvxHWZmZmYjWk2StYjoADrS8jPUaHyHmZmZ2UhX3NHlNuJNrvEFPhdM7eHshbexacnJNd2vmZlZPfneoGZmZmYF5mTNzMzMrMCcrJmZmZkVmJM1MzMzswJzsmZmZmZWYE7WzMzMzArMyZqZmZlZgTlZMzMzMyswJ2tmZmZmBeZkzczMzKzAnKyZmZmZFZiTNTMzM7MCc7JmZmZmVmBO1szMzMwKzMmamZmZWYE5WTMzMzMrMCdrZmZmZgXmZM3MzMyswJysmZmZmRWYkzUzMzOzAnOyZmZmZlZgTtbMzKogaZOkNZIekrQ6lR0saaWkR9LPg3LbL5LUKWmDpBNz5cen/XRKulSSmtEeMxs+nKyZmVXvXRFxbERMS88XAndFxBTgrvQcSUcBc4CjgVnA5ZJGpTpXAPOBKekxq4Hxm9kwNOhkTdIkSd+TtF7SWknnp3J/0zSzkWI2sCwtLwNOyZXfGBEvR8RGoBOYLmk8MDYi7o2IAK7J1TEzq2j0EOr2AAsi4kFJ+wMPSFoJnE32TXOJpIVk3zQ/UfZN8zDgu5LeHBE72fVN8z7gdrJvmncMITYzs1oL4E5JAfx7RCwF2iJiK0BEbJV0aNp2All/VtKVyl5Jy+Xlu5E0n6xPpK2tjY6Ojj4D6+7upqOjgwVTewbTrrppG0O/MfXXtlorvVdFUsSYoJhxjdSYBp2spQ6q1EntkLSerNOZDbSnzZYBHcAnyH3TBDZKKn3T3ET6pgkgqfRN08mamRXJCRGxJSVkKyX9rI9tK50diD7Kdy/IEsGlANOmTYv29vY+A+vo6KC9vZ2zF97W53aNtmBqDxet6effzJoX6/Lam5acXLG89F4VSRFjgmLGNVJjGsqRtd+SNBk4DlhFnb5pptcZ0LfNkmq+bVbzDbDVjLQ2l9p72XW31mX/UyccUJf9DlYRv4EOZxGxJf3cJulbwHTgSUnjU183HtiWNu8CJuWqTwS2pPKJFcrNzHo15GRN0n7AN4ELIuKFPoabDembJgz822ZJNd82q/oG2GJGWpvr3d5NZ7TXbd+DUcRvoMOVpH2BV6WzCPsC7wX+EVgBzAWWpJ+lbwIrgOslXUw27GMKcH9E7JS0Q9IMsi+3ZwGXNbY1ZjbcDOk/l6S9yBK16yLillTsb5pm1mragG+lL6Ojgesj4juSfgQslzQPeBw4DSAi1kpaDqwjG997bhqfC3AOcDUwhmy4h4d8mFmfBp2spRmbVwLrI+Li3Cp/0zSzlhIRjwFvrVD+DDCzlzqLgcUVylcDx9Q6RjNrXUM5snYCcCawRtJDqeyTZEmav2mamZmZ1cBQZoP+kMrjzcDfNM3MzMxqwncwMDMzMyswJ2tmZmZmBeZkzczMzKzAnKyZmZmZFZiTNTMzM7MCc7JmZmZmVmBO1szMzMwKzMmamZmZWYE5WTMzMzMrMCdrZmZmZgXmZM3MzMyswJysmZmZmRWYkzUzMzOzAnOyZmZmZlZgTtbMzMzMCszJmpmZmVmBOVkzMzMzK7DRzQ7ArFVMXnhb3fa9acnJddu3mZkVm4+smZmZmRWYkzUzMzOzAnOyZmZmZlZgTtbMzMzMCswTDMzMrOX0NuFnwdQezh7CZCBP9rFmKEyyJmkW8G/AKOCrEbGkySGZFcZgZppW80/J/3iaw/2dmQ1EIU6DShoFfAl4H3AUcLqko5oblZlZ7bm/M7OBKsqRtelAZ0Q8BiDpRmA2sK6pUZm1uHpdG85H7Prk/m4Yq8ffzIKpPbTXfK/WShQRzY4BSacCsyLiY+n5mcDvRcR5ZdvNB+anp0cCG2oYxjjg6RrubzgYaW12e4vpDRHx2mYH0SjV9HeD6OuK+rsuYlyOqXpFjGs4xzTovq4oR9ZUoWyPLDIilgJL6xKAtDoiptVj30U10trs9lpB9NvfDbSvK+rvuohxOabqFTGukRpTIcasAV3ApNzzicCWJsViZlZP7u/MbECKkqz9CJgi6XBJewNzgBVNjsnMrB7c35nZgBTiNGhE9Eg6D/gvsqnsV0XE2gaHUZfTqwU30trs9lrT1am/K+rvuohxOabqFTGuERlTISYYmJmZmVllRTkNamZmZmYVOFkzMzMzK7ARmaxJmiTpe5LWS1or6fxUfrCklZIeST8PanastSRplKQfS/p2et6y7ZV0oKSbJf0s/Z7f0crtBZD0V+nz/LCkGyS9utXbPBJImiVpg6ROSQsrrJekS9P6n0p6W7V16xjTGSmWn0r6H0lvza3bJGmNpIckra5VTFXG1S7p+fTaD0n6h2rr1jGmv83F87CknZIOTuvq8l5JukrSNkkP97K+GZ+p/mJq+Geqipga93mKiBH3AMYDb0vL+wP/S3bbl88DC1P5QuDCZsda43b/NXA98O30vGXbCywDPpaW9wYObPH2TgA2AmPS8+XA2a3c5pHwIJuA8CjwxvQ5/glwVNk2JwF3kF2/bQawqtq6dYzp94GD0vL7SjGl55uAcU16r9pL/d9A69YrprLtPwDc3YD36p3A24CHe1nf0M9UlTE14zPVX0wN+zyNyCNrEbE1Ih5MyzuA9WT/7GaT/ZMn/TylKQHWgaSJwMnAV3PFLdleSWPJ/siuBIiIX0fEc7Roe3NGA2MkjQZeQ3btrlZvc6v77a2pIuLXQOnWVHmzgWsicx9woKTxVdatS0wR8T8RsT09vY/sWnL1NpT2Nu29KnM6cEMNXrdPEXEP8GwfmzT6M9VvTM34TFXxPvWm5u/TiEzW8iRNBo4DVgFtEbEVsoQOOLSJodXaF4CPA7/JlbVqe98IPAV8Tdlp369K2pfWbS8R8QTwr8DjwFbg+Yi4kxZu8wgxAdice96VyqrZppq69Yopbx7ZUZqSAO6U9ICy22rVSrVxvUPSTyTdIenoAdatV0xIeg0wC/hmrrhe71V/Gv2ZGqhGfaaq0ZDPUyGus9YskvYj+8O4ICJekCrdBWb4k/R+YFtEPCCpvcnhNMJoskPXfxERqyT9G9kpwJaVxqLNBg4HngO+IekjTQ3KaqGaW/H1tk1Vt/EbhKr3K+ldZP9Y/yBXfEJEbJF0KLBS0s/SEYxGxPUg2f0ZuyWdBPwHMKXKuvWKqeQDwH9HRP5ITr3eq/40+jNVtQZ/pvrTsM/TiD2yJmkvskTtuoi4JRU/mQ71kn5ua1Z8NXYC8EFJm8gOx75b0tdp3fZ2AV0RsSo9v5kseWvV9gK8B9gYEU9FxCvALWRjPFq5zSNBNbem6m2bet3Wqqr9SvpdsmEXsyPimVJ5RGxJP7cB3yI7ZVQL/cYVES9ERHdavh3YS9K4aurWK6acOZSdAq3je9WfRn+mqtKEz1SfGvl5GpHJmrJDaFcC6yPi4tyqFcDctDwXuLXRsdVDRCyKiIkRMZmsQ7g7Ij5C67b3F8BmSUemopnAOlq0vcnjwAxJr0mf75lkYzFbuc0jQTW3ploBnJVm8M0gOwW+tcq6dYlJ0uvJvjCcGRH/myvfV9L+pWXgvUDFmXZ1iut16e8DSdPJ/gc+U03desWUYjkA+CNyf591fq/60+jPVL+a9JnqL6aGfZ5G6mnQE4AzgTWSHkplnwSWAMslzSP753dac8JrmFZu718A16U/lMeAPyH7Q2rJ9qbTvTeTHZbvAX5MdguU/WjRNo8E0cutqST9eVr/ZeB2stl7ncAvyT7rvdZtUEz/ABwCXJ7+l/VExDSgDfhWKhsNXB8R3xlqTAOI61TgHEk9wK+AOZFN32vmewXwIeDOiHgxV71u75WkG8hmMo6T1AV8CtgrF1NDP1NVxtTwz1QVMTXs8+TbTZmZmZkV2Ig8DWpmZmY2XDhZMzMzMyswJ2tmZmZmBeZkzczMzKzAnKyZmZmZ9UL93NC9wvYflrRO0lpJ19ckBs8GNTMzM6tM0juBbrL7pR7Tz7ZTgOXAuyNiu6RD08V6h8RH1szMzMx6UemG7pLeJOk7yu5H+gNJv5NW/SnwpdJN52uRqIGTNTMzM7OBWkp2/+njgb8BLk/lbwbeLOm/Jd0naVYtXmyk3sHAzMzMbMAk7Ud27+VvpDsnAOyTfo4mu5l7O9k9QX8g6ZiIeG4or+lkzczMzKx6rwKei4hjK6zrAu6LiFeAjZI2kCVvPxrqC5qZmZlZFSLiBbJE7DSAdMP7t6bV/wG8K5WPIzst+thQX9PJmpmZmVkv0g3d7wWOlNQlaR5wBjBP0k+AtcDstPl/Ac9IWgd8D/jbiHhmyDH40h1mZmZmxeUjayOcpHZJXU143U9K+moDXqdD0sfq/TpmZgCS/jCNUxps/aslfa6WMeX2fYekufXYt9WXJxhY3UlqB74eERNLZRHxT00LyMysTiLiB8CRpeeSNgEfi4jvNi2oJCLe1+wYbHB8ZM3MzMyswJystShJh0n6pqSnJG2U9JepfEw6zL49DYB8e1m9kHRE7vluh+QlzZb0kKQXJD1auuCfpD+RtF7SDkmPSfqzVL4vcAdwmKTu9DhM0qclfT233w+m+6g9l05dviW3bpOkv5H0U0nPS7pJ0qvTuoMkfTu1c3ta/u0RvCrep5D055IeSfW/pHThnAoxTk7bj07POyR9TtL/pHb9p6RDJF2X3p8fSZpcbSxm1niSJkm6JfUhz0j6Yro6/d3p+dPpb/rAXJ1Nkhal+z9ul/S1XJ/026Elkq4FXg/8Z+ojPp7KvyHpF6k/u0fS0QOI922Sfpz62m+k/vBzaV2f/WF+WIiksyX9UNK/pm03SvKRt4JystaCJL0K+E/gJ8AEYCZwgaQTgU8Bb0qPE4Gqxy9Img5cA/wtcCDwTmBTWr0NeD8wFvgT4BJJb4uIF4H3AVsiYr/02FK23zcDNwAXAK8Fbifr3PbObfZhYBZwOPC7wNmp/FXA14A3kHWKvwK+WG2bkveTJa1vTa9z4gDqzgHOJHuf30Q2Y+hrwMHAerL328wKSNIo4NvAz4HJZH/HNwIC/hk4DHgLMAn4dFn1M8j6ijeRXZ7h78v3HxFnAo8DH0h93+fTqjvIrr11KPAgcF2V8e4NfAu4mqyPuQH4UG6TgfaHvwdsAMYBnweuLH1ZtWJxstaa3g68NiL+MSJ+HRGPAV8hSyw+DCyOiGcjYjNw6QD2Ow+4KiJWRsRvIuKJiPgZQETcFhGPRub7wJ3AH1a53/8D3Jb2+wrwr8AYsitEl1waEVsi4lmyRPTY9LrPRMQ3I+KXEbEDWAz80QDaBLAkIp6LiMfJplofO4C6X0vtfp6sA340Ir4bET3AN4DjBhiLmTXOdLKE7G8j4sWIeCkifhgRnak/ejkingIuZs9+5YsRsTn1SYuB06t90Yi4KiJ2RMTLZEngWyUdUEXVGWRjzS+NiFci4hbg/tx+B9of/jwivhIRO4FlwHigrdp2WOM4WWtNbyA77fhc6QF8kuyP8DBgc27bnw9gv5OARyutkPQ+ZfdBeza93klk39aqcVg+joj4TYpxQm6bX+SWfwnsl173NZL+XdLPJb0A3AMcmL4xl8e4NncqNp9IVtx3lZ7MLf+qwvOB7MvMGmsSWcLSky+UdKikGyU9kfqVr7Nnf1bejx5WzQtKGiVpibJhJC+w6+zEHv2lstmbpT7rjPQaT8Tu19zanNu+6v4w+W3fFxG/TIvuswrIyVpr2gxsjIgDc4/9I+IkYCtZB1Xy+rK6vwRek3v+urL9vqn8xSTtA3yT7IhYW0QcSHYqs3Q4vb+L+W0hSzBL+1OK8Yl+6gEsIJt59XsRMZbs1Cy51/6tiDg6dyr2B1Xs+0V6fy/MbPjbDLy+NA4155/J+q3fTf3KR9izTynvR7dQWXn/93/JLqD6HuAAstOvVNg/EfG+XJ91HVn/PaHsVGU+jqr7QxtenKy1pvuBFyR9QtmEglGSjpH0dmA5sCgNRJ0I/EVZ3YeA/5vqzGL3Q+hXAn8iaaakV0maIOl3gL3JbmL7FNCTBqm+N1fvSeCQPg7zLwdOTvvdi6zDeRn4nyrauj/ZEaznJB1MbceIPQS8U9LrU+yLarhvM2u++8kSoCWS9pX0akknkPUr3WT9ygSycbrlzpU0MfU7nwRu6uU1ngTemHu+P1n/9gzZl8GBXMboXmAncJ6k0ZJmk53Kze+7Xv2hNZGTtRaUxh98gGzs1UbgaeCrZN/iPkN2yH4j2biya8uqn5/qPkc2gPY/cvu9nzR5AHge+D7whjQ24i/Jkq7tZN8cV+Tq/YxsIOxj6bTsbqcLImID2TfXy1KsHyAbkPvrKpr7BbLxbU8D9wHfqaJOVSJiJVkH/FPgAbKByGbWInJ95RFkEwG6yMbQfgZ4G1k/dxtwS4Xq15P1oY+lR28Xsv1n4O9T3/c3ZJO0fk525mAdWb9Vbby/Bv5/ZOOHnyPrN79NlvxBHftDay7fbsrMzGwAVKAL3UpaBXw5Ir7W7FisfnxkzczMbJiQ9EeSXpdOg84lu5SRj6C1ON9uyszMbPg4kmzIyX5ks/NPjYitzQ3J6s2nQc3MzMwKzKdBzczMzArMyZqZWYFJ8ngksxYwlL/lYTtmbdy4cTF58uSmxvDiiy+y7777NjWGco6pf0WLB0Z2TA888MDTEfHaur/QMDV27NgTp02bVtV4lSJ+jvoz3GJ2vPXV4vG+MOgXiohh+Tj++OOj2b73ve81O4Q9OKb+FS2eiJEdE7A6CtCnFPUxkL6uiJ+j/gy3mB1vfbVyvEPp6/o9DSrpKknbJD2cK/t0umfaQ+lxUm7dIkmdkjZIOjFXfrykNWndpaXbZUjaR9JNqXyVpMmDzjzNzMzMWkw1Y9auBmZVKL8kIo5Nj9sBJB0FzAGOTnUuz91A9gpgPjAlPUr7nAdsj4gjyK6Mf+Eg22JmZmbWcvpN1iLiHuDZKvc3G7gxIl6OiI1AJzBd0nhgbETcmw4FXgOckquzLC3fDMwsu0mtmZmZ2Yg1lAkG50k6C1gNLIiI7cAEdr/PWVcqeyUtl5eTfm4GiIgeSc8Dh5Dd22w3kuaTHZ2jra2Njo6OIYQ/dN3d3U2PoZxj6l/R4gHHZGZmvRtssnYF8Fkg0s+LgI8ClY6IRR/l9LNu98KIpcBSgGnTpkV7e/uAgq61jo4Omh1DOcfUv6LFA47JzMx6N6hkLSKeLC1L+grw7fS0C5iU23QisCWVT6xQnq/TJWk0cADVn3a1FjZ54W112e/Vs4bPtHCzgVjzxPOcXae/m01LTq7Lfs2sf4O6KG4ag1byIaA0U3QFMCfN8DycbCLB/ZHdt2yHpBlpPNpZwK25OnPT8qnA3Wlcm5mZmdmI1++RNUk3AO3AOEldwKeAdknHkp2u3AT8GUBErJW0HFgH9ADnRsTOtKtzyGaWjgHuSA+AK4FrJXWSHVGbU4N2mZmZmbWEfpO1iDi9QvGVfWy/GFhcoXw1cEyF8peA0/qLw8zMzGwk8r1BzczMzArMyZqZmZlZgTlZMzMzMyswJ2tmZmZmBeZkzczMzKzAnKyZmZmZFZiTNTOzHEmjJP1Y0rfT84MlrZT0SPp5UG7bRZI6JW2QdGKu/HhJa9K6S9PFwEkXDL8pla+SNLnhDTSzYcfJmpnZ7s4H1ueeLwTuiogpwF3pOZKOIruI99HALOBySaNSnSuA+WR3cZmS1gPMA7ZHxBHAJcCF9W2KmbUCJ2tmZomkicDJwFdzxbOBZWl5GXBKrvzGiHg5IjYCncD0dDu+sRFxb7p13jVldUr7uhmYWTrqZmbWm0HdyN3MrEV9Afg4sH+urC3d35iI2Crp0FQ+Abgvt11XKnslLZeXl+psTvvqkfQ8cAjwdD4ISfPJjszR1tZGR0dHVcG3jYEFU3uq2nagqo1hoLq7u+u273pwvPXleCtzsmZmBkh6P7AtIh6Q1F5NlQpl0Ud5X3V2L4hYCiwFmDZtWrS3VxMOXHbdrVy0pj7d+qYzqothoDo6Oqi2fUXgeOvL8VbmZM3MLHMC8EFJJwGvBsZK+jrwpKTx6ajaeGBb2r4LmJSrPxHYksonVijP1+mSNBo4AHi2Xg0ys9bgMWtmZkBELIqIiRExmWziwN0R8RFgBTA3bTYXuDUtrwDmpBmeh5NNJLg/nTLdIWlGGo92Vlmd0r5OTa+xx5E1M7M8H1kzM+vbEmC5pHnA48BpABGxVtJyYB3QA5wbETtTnXOAq4ExwB3pAXAlcK2kTrIjanMa1QgzG76crJmZlYmIDqAjLT8DzOxlu8XA4grlq4FjKpS/REr2zMyq1e9pUElXSdom6eFc2b9I+pmkn0r6lqQDU/lkSb+S9FB6fDlXxxeJNDMzMxugasasXc2uCzqWrASOiYjfBf4XWJRb92hEHJsef54r90UizczMzAao32QtIu6hbLZSRNwZEaWL+dzH7jOf9uCLRJqZmZkNTi3GrH0UuCn3/HBJPwZeAP4+In5AdiHIIV0kEgZ/och6KeLF+1oppnpd3LOV3qN6KmJMZmYj0ZCSNUl/RzYL6rpUtBV4fUQ8I+l44D8kHU0NLhIJg79QZL0U8eJ9rRTT2Qtvq30wwNWz9m2Z96ieihiTmdlINOhkTdJc4P3AzNJ1giLiZeDltPyApEeBN+OLRJqZmZkNyqAuiitpFvAJ4IMR8ctc+WsljUrLbySbSPCYLxJpZmZmNjj9HlmTdAPQDoyT1AV8imz25z7AyjQX4L408/OdwD9K6gF2An8eEaWjZL5IpJmZmdkA9ZusRcTpFYqv7GXbbwLf7GWdLxJpZmZmNkC+g4GNOGueeL4ukxc2LTm55vs0MzPzjdzNzMzMCszJmpmZmVmBOVkzMzMzKzAna2ZmZmYF5mTNzMzMrMCcrJmZmZkVmJM1MzMzswJzsmZmZmZWYE7WzMzMzArMyZqZmZlZgTlZMzMzMyswJ2tmZmZmBeZkzczMzKzAnKyZmZmZFVi/yZqkqyRtk/RwruxgSSslPZJ+HpRbt0hSp6QNkk7MlR8vaU1ad6kkpfJ9JN2UyldJmlzjNpqZ9UvSqyXdL+knktZK+kwqd39nZk1VzZG1q4FZZWULgbsiYgpwV3qOpKOAOcDRqc7lkkalOlcA84Ep6VHa5zxge0QcAVwCXDjYxpiZDcHLwLsj4q3AscAsSTNwf2dmTTa6vw0i4p4K3/5mA+1peRnQAXwild8YES8DGyV1AtMlbQLGRsS9AJKuAU4B7kh1Pp32dTPwRUmKiBhso8xazeSFt9Vlv5uWnFyX/Q5Hqc/pTk/3So/A/Z2ZNVm/yVov2iJiK0BEbJV0aCqfANyX264rlb2SlsvLS3U2p331SHoeOAR4uvxFJc0n+7ZKW1sbHR0dgwy/Nrq7u5seQ7lWimnB1J7aBwO0janPvofyvvf3HtXrvejrNYv4Waq3dGTsAeAI4EsRsUpSw/u7wfZ19fpsw9A+330Zbp8zx1tfjreywSZrvVGFsuijvK86exZGLAWWAkybNi3a29sHEWLtdHR00OwYyrVSTGfX6WjSgqk9XLSm1h992HRG+6Dr9vce1eu96CvmIn6W6i0idgLHSjoQ+JakY/rYvG793WD7usuuu7Uun20Y2ue7L8Ptc+Z468vxVjbY2aBPShoPkH5uS+VdwKTcdhOBLal8YoXy3epIGg0cADw7yLjMzIYsIp4jO905C/d3ZtZkg03WVgBz0/Jc4NZc+Zw04+lwsoG196dTCDskzUizos4qq1Pa16nA3R6/YWaNJum16YgaksYA7wF+hvs7M2uyfo+XS7qBbHDtOEldwKeAJcBySfOAx4HTACJiraTlwDqgBzg3nVYAOIdsZukYsoG2d6TyK4Fr0+DcZ8lmV5mZNdp4YFkat/YqYHlEfFvSvbi/M7MmqmY26Om9rJrZy/aLgcUVylcDe4z/iIiXSJ2fmVmzRMRPgeMqlD+D+zszayLfwcDMzMyswJysmZmZmRWYkzUzMzOzAnOyZmZmZlZgTtbMzMzMCszJmpmZmVmBOVkzMzMzKzAna2ZmZmYF5mTNzMzMrMCcrJmZmZkVmJM1MzMzswJzsmZmZmZWYE7WzMzMzArMyZqZmZlZgTlZMzMzMyswJ2tmZmZmBTboZE3SkZIeyj1ekHSBpE9LeiJXflKuziJJnZI2SDoxV368pDVp3aWSNNSGmZmZmbWCQSdrEbEhIo6NiGOB44FfAt9Kqy8prYuI2wEkHQXMAY4GZgGXSxqVtr8CmA9MSY9Zg43LzMzMrJXU6jToTODRiPh5H9vMBm6MiJcjYiPQCUyXNB4YGxH3RkQA1wCn1CguMzMzs2FtdI32Mwe4Iff8PElnAauBBRGxHZgA3JfbpiuVvZKWy8v3IGk+2RE42tra6OjoqFH4g9Pd3d30GMq1UkwLpvbUPhigbUx99j2U972/96he70Vfr1nEz5KZ2Ug05GRN0t7AB4FFqegK4LNApJ8XAR8FKo1Diz7K9yyMWAosBZg2bVq0t7cPJfQh6+jooNkxlGulmM5eeFvtgyFLfC5aU6vvKbtsOqN90HX7e4/q9V70FXMRP0tmZiNRLU6Dvg94MCKeBIiIJyNiZ0T8BvgKMD1t1wVMytWbCGxJ5RMrlJuZmZmNeLVI1k4ndwo0jUEr+RDwcFpeAcyRtI+kw8kmEtwfEVuBHZJmpFmgZwG31iAuMzMzs2FvSMmapNcAfwzckiv+fLoMx0+BdwF/BRARa4HlwDrgO8C5EbEz1TkH+CrZpINHgTuGEpeZ2UBJmiTpe5LWS1or6fxUfrCklZIeST8PytUZ0OWI0pfVm1L5KkmTG95QMxt2hjRwJyJ+CRxSVnZmH9svBhZXKF8NHDOUWMzMhqiHbELUg5L2Bx6QtBI4G7grIpZIWggsBD5Rdjmiw4DvSnpz+hJauhzRfcDtZJcjugOYB2yPiCMkzQEuBP5PQ1tpZsOO72BgZgZExNaIeDAt7wDWk81Mnw0sS5stY9elhQZzOaL8vm4GZvoi4GbWn9pPiTMzG+bS6cnjgFVAWxpbS0RslXRo2mwwlyOaAGxO++qR9DzZ2Ymny15/UJcpqtdlaWBol6bpy3C7RIzjrS/HW5mTNTOzHEn7Ad8ELoiIF/o48DWYyxFVdamiwV6m6LLrbq3LZWlgaJem6ctwu0SM460vx1uZT4OamSWS9iJL1K6LiNLEqSdLs9zTz22pfDCXI/ptHUmjgQOAZ2vfEjNrJU7WzMyANHbsSmB9RFycW7UCmJuW57Lr0kKDuRxRfl+nAnencW1mZr3yaVAzs8wJwJnAGkkPpbJPAkuA5ZLmAY8Dp0F2OSJJpcsR9bDn5YiuBsaQzQItXY7oSuBaSZ1kR9Tm1LlNZtYCnKyZmQER8UMqjykDmNlLnQFdjigiXiIle2Zm1fJpUDMzM7MCc7JmZmZmVmBO1szMzMwKzMmamZmZWYE5WTMzMzMrMCdrZmZmZgXmZM3MzMyswJysmZmZmRXYkJI1SZskrZH0kKTVqexgSSslPZJ+HpTbfpGkTkkbJJ2YKz8+7adT0qXq487JZmZmZiNJLY6svSsijo2Iaen5QuCuiJgC3JWeI+koslurHA3MAi6XNCrVuQKYT3ZvvSlpvZmZmdmIV4/ToLOBZWl5GXBKrvzGiHg5IjYCncB0SeOBsRFxb7qh8TW5OmZmZmYj2lDvDRrAnZIC+PeIWAq0RcRWgIjYKunQtO0E4L5c3a5U9kpaLi/fg6T5ZEfgaGtro6OjY4jhD013d3fTYyjXSjEtmNpT+2CAtjH12fdQ3vf+3qN6vRd9vWYRP0tmZiPRUJO1EyJiS0rIVkr6WR/bVhqHFn2U71mYJYNLAaZNmxbt7e0DDLe2Ojo6aHYM5VopprMX3lb7YMgSn4vWDPWjv6dNZ7QPum5/71G93ou+Yi7iZ8nMbCQa0mnQiNiSfm4DvgVMB55MpzZJP7elzbuASbnqE4EtqXxihXIzMzOzEW/QyZqkfSXtX1oG3gs8DKwA5qbN5gK3puUVwBxJ+0g6nGwiwf3plOkOSTPSLNCzcnXMzMzMRrShnAtqA76VrrIxGrg+Ir4j6UfAcknzgMeB0wAiYq2k5cA6oAc4NyJ2pn2dA1wNjAHuSA8zMzOzEW/QyVpEPAa8tUL5M8DMXuosBhZXKF8NHDPYWMzMzMxale9gYGZmZlZgTtbMzMzMCszJmpmZmVmBOVkzMzMzKzAna2ZmZmYF5mTNzMzMrMCcrJmZmZkVmJM1M7NE0lWStkl6OFd2sKSVkh5JPw/KrVskqVPSBkkn5sqPl7Qmrbs03Z2FdAeXm1L5KkmTG9pAMxuWnKyZme1yNTCrrGwhcFdETAHuSs+RdBQwBzg61blc0qhU5wpgPtlt9abk9jkP2B4RRwCXABfWrSVm1jKcrJmZJRFxD/BsWfFsYFlaXgackiu/MSJejoiNQCcwXdJ4YGxE3BsRAVxTVqe0r5uBmaWjbmZmvRnKvUHNzEaCtojYChARWyUdmsonAPfltutKZa+k5fLyUp3NaV89kp4HDgGezr+gpPlkR+Zoa2ujo6OjukDHwIKpPVU3bCCqjWGguru767bvenC89eV4K3OyZmY2OJWOiEUf5X3V2b0gYimwFGDatGnR3t5eVUCXXXcrF62pT7e+6YzqYhiojo4Oqm1fETje+nK8lfk0qJlZ355MpzZJP7el8i5gUm67icCWVD6xQvludSSNBg5gz9OuZma7cbJmZta3FcDctDwXuDVXPifN8DycbCLB/emU6Q5JM9J4tLPK6pT2dSpwdxrXZmbWK58GNTNLJN0AtAPjJHUBnwKWAMslzQMeB04DiIi1kpYD64Ae4NyI2Jl2dQ7ZzNIxwB3pAXAlcK2kTrIjanMa0CwzG+YGnaxJmkQ2y+l1wG+ApRHxb5I+Dfwp8FTa9JMRcXuqs4hs6vpO4C8j4r9S+fHs6thuB873t00za7SIOL2XVTN72X4xsLhC+WrgmArlL5GSPTOzag3lyFoPsCAiHpS0P/CApJVp3SUR8a/5jcuuSXQY8F1Jb07fREvXJLqPLFmbxa5vomZmZmYj1qDHrEXE1oh4MC3vANaza3p6JYO5JpGZmZnZiFaTMWvplinHAauAE4DzJJ0FrCY7+radwV2TqPx1BnXtoXop4vVgWimmel0vql7XohrK+97fe9SMa2cV8bNkZjYSDTlZk7Qf8E3ggoh4QdIVwGfJrh30WeAi4KMM7ppEuxcO8tpD9VLE68G0UkxnL7yt9sGQJT71uBbVUK5D1d97VK/3oq+Yi/hZMjMbiYZ06Q5Je5ElatdFxC0AEfFkROyMiN8AXwGmp80Hc00iMzMzsxFtKLNBRTYNfX1EXJwrH1+6NQvwIeDhtLwCuF7SxWQTDErXJNopaYekGWSnUc8CLhtsXNZ4k/s56rNgak/djgyZmZm1uqGcCzoBOBNYI+mhVPZJ4HRJx5KdytwE/BkM+ppEZmZmZiPaoJO1iPghlceb3d5HnQFdk8jMzMxspPPtpszMzMwKzMmamZmZWYE5WTMzMzMrMCdrZmZmZgVW+yuD2pD1dymMvvR1mYxNS04e9H6tf/X6vZmZ2cjmI2tmZmZmBeYja2YjWF9HA4dytM9Hcc3MasfJ2ggylNN0ZmZm1hw+DWpmZmZWYE7WzMzMzArMyZqZmZlZgTlZMzMzMyswJ2tmZmZmBeZkzczMzKzAfOmOIVjzxPO+6ryZmZnVVWGOrEmaJWmDpE5JC5sdj5lZvbi/M7OBKMSRNUmjgC8Bfwx0AT+StCIi1tVi//W6GOyCqXXZrZm1sHr3d/VSr3706ln71mW/Zq2kKEfWpgOdEfFYRPwauBGY3eSYzMzqwf2dmQ1IIY6sAROAzbnnXcDvNSkWM7N6cn+XM9zG/g7lnrl98f1062u4HxlWRDTkhfoMQjoNODEiPpaenwlMj4i/KNtuPjA/PT0S2NDQQPc0Dni6yTGUc0z9K1o8MLJjekNEvLYBr1MI1fR3Q+jrivg56s9wi9nx1lcrxzvovq4oR9a6gEm55xOBLeUbRcRSYGmjguqPpNURMa3ZceQ5pv4VLR5wTCNMv/3dYPu64fg7G24xO976cryVFWXM2o+AKZIOl7Q3MAdY0eSYzMzqwf2dmQ1IIY6sRUSPpPOA/wJGAVdFxNomh2VmVnPu78xsoAqRrAFExO3A7c2OY4AKc0o2xzH1r2jxgGMaUerY3w3H39lwi9nx1pfjraAQEwzMzMzMrLKijFkzMzMzswqcrPVD0iRJ35O0XtJaSedX2KZd0vOSHkqPf2hAXJskrUmvt7rCekm6NN3O5qeS3lbHWI7Mtf0hSS9IuqBsm7q/R5KukrRN0sO5soMlrZT0SPp5UC9163L7n15i+hdJP0u/l29JOrCXun3+jmsc06clPZH7/ZzUS13fJqmgmvW76a2P7OtvT9KiFOcGSSfmyo9Pn/nO1H8ple8j6aZUvkrS5BrEPUrSjyV9e5jEe6Ckm1PfsV7SO4ocs6S/Sp+HhyXdIOnVRYq3l36wIfFJmpte4xFJc6sKOCL86OMBjAfelpb3B/4XOKpsm3bg2w2OaxMwro/1JwF3AAJmAKsaFNco4Bdk15Np6HsEvBN4G/BwruzzwMK0vBC4sJeYHwXeCOwN/KT8d1zjmN4LjE7LF1aKqZrfcY1j+jTwN1X8buvyPvkx5N9p0343vfWRvf3tpXU/AfYBDk9xj0rr7gfekfqtO4D3pfL/B/hyWp4D3FSDuP8auL7ULw2DeJcBH0vLewMHFjVmsgs/bwTGpOfLgbOLFC8D+H9Ry/iAg4HH0s+D0vJB/cXrI2v9iIitEfFgWt4BrCf7IBbdbOCayNwHHChpfANedybwaET8vAGvtZuIuAd4tqx4NlknR/p5SoWqdbv9T6WYIuLOiOhJT+8ju85Ww/TyPlXDt0kqrqb9bvroI3v725sN3BgRL0fERqATmJ76p7ERcW9k/9WuKatT2tfNwMzSEYzBkDQROBn4aq64yPGOJUsurgSIiF9HxHNFjplsAuMYSaOB15BdS7Aw8Q7w/0Ut4zsRWBkRz0bEdmAlMKu/eJ2sDUA6jHkcsKrC6ndI+omkOyQd3YBwArhT0gPKrnZertItbRqRZM4BbuhlXaPfI4C2iNgK2T8V4NAK2zTrvQL4KNm3sUr6+x3X2nnKTs1epcqni5v5PlnfCvG7Kesje/vb6y3WCWm5vHy3OumLzvPAIUMI9QvAx4Hf5MqKHO8bgaeAryk7dftVSfsWNeaIeAL4V+BxYCvwfETcWdR4cxoR36D+Vp2sVUnSfsA3gQsi4oWy1Q+SnfZ7K3AZ8B8NCOmEiHgb8D7gXEnvLFtf6RtGXaf+KrvA5weBb1RY3Yz3qFoNf68AJP0d0ANc18sm/f2Oa+kK4E3AsWSd60UVtmnK+2RVafrvpp8+crdNK5RFH+V91RkwSe8HtkXEA9VW6eW1GxJvMprslN0VEXEc8CLZabreNPs9PojsyNLhwGHAvpI+0leVXl67ke9xX2oZ36DidrJWBUl7kXVC10XELeXrI+KFiOhOy7cDe0kaV8+YImJL+rkN+BbZaZC8qm7hVWPvAx6MiCfLVzTjPUqeLJ3+TT+3Vdim4e9VGlT6fuCMdPh8D1X8jmsmIp6MiJ0R8RvgK728VjM+U1adpv5ueukje/vb6y3WLnYfEpBvw2/rpNNqBzC4U/kAJwAflLSJ7HTxuyV9vcDxlvbXFRGlszo3kyVvRY35PcDGiHgqIl4BbgF+v8DxljQivkH9rTpZ60c6x3wlsD4iLu5lm9flZoBMJ3tfn6ljTPtK2r+0TDZg/eGyzVYAZykzg+ww9NZ6xZScTi+nQBv9HuWsAEqzbeYCt1bYpqG3/5E0C/gE8MGI+GUv21TzO65lTPnxjB/q5bV8m6Tiatrvpo8+sre/vRXAnDRb7nBgCnB/6p92SJqR9nlWWZ3Svk4F7u7tS05/ImJRREyMiMlk79PdEfGRosabYv4FsFnSkaloJrCuwDE/DsyQ9Jr0OjPJxjIWNd6SRsT3X8B7JR2UjkC+N5X1LWo8M6jVHsAfkB2i/CnwUHqcBPw58Odpm/OAtWSzRe4Dfr/OMb0xvdZP0uv+XSrPxyTgS2SzVtYA0+oc02vIkq8DcmUNfY/IEsWtwCtk317mkY0RuAt4JP08OG17GHB7ru5JZLPYHi29n3WMqZNszELp8/Tl8ph6+x3XMaZr0+fkp2SdzPhGvk9+1OT32pTfTR99ZMW/vVTn71KcG0iz51L5NLIvCo8CX2TXhdtfTTa8opNs9t0baxR7O7tmgxY6XrIhCqvT+/wfZDMJCxsz8BngZ+m1riWbSVmYeBnA/4tax0c2VrkzPf6kmnh9BwMzMzOzAvNpUDMzM7MCc7JmZmZmVmBO1szMzMwKzMmamZmZWYE5WTMzMzMrMCdrZmZmZgXmZM3MzMyswJysmZmZmRWYkzUzMzOzAnOyZmZmZlZgTtbMzMzMCszJmpmZmVmBOVkzMzMzKzAna2ZmZmYF5mTNGkbSH0raMIT6V0v6XB/rN0l6z2D3b2bDn/uB2pN0tqQfNjuOkczJmjVMRPwgIo4sPXenamZm1j8na2ZmZgMgaXQTX3tUs17bmsfJmvVK0iRJt0h6StIzkr4o6U2S7k7Pn5Z0naQDc3U2SVokaZ2k7ZK+JunVaV27pK60fC3weuA/JXVL+ngq/4akX0h6XtI9ko4eZOz7SPqCpC3p8QVJ+6R14yR9W9Jzkp6V9ANJr0rrPiHpCUk7JG2QNHNIb6KZNcOxkn6a+pGbcn3Qn0rqTH/3KyQdlsonS4p8EiapQ9LH0vLZkv5b0iWSngU+LekISd9Pr/G0pJt6CyYN4fiypJWpb/m+pDfk1v9OWvds6nc+XFb3Ckm3S3oReFfZvj8j6bK0vJekFyV9Pj0fI+klSQel5zMk/U/q+34iqT23nwMkXSlpa+oDP9dbYijpXyT9UNIB1f5CbGicrFlF6Y/028DPgcnABOBGQMA/A4cBbwEmAZ8uq34GcCLwJuDNwN+X7z8izgQeBz4QEftFxOfTqjuAKcChwIPAdYNswt8BM4BjgbcC03NxLAC6gNcCbcAngZB0JHAe8PaI2D+1YdMgX9/MmufDwCzgcOB3gbMlvZus7/owMJ6sb7txAPv8PeAxsr5pMfBZ4E7gIGAicFk/9c9IdcYBD5H6Nkn7AiuB69O+TwcuL/ui+n/Ta+4PlI8d+z7QnpbfDvwC+KP0/B3AhojYLmkCcBvwOeBg4G+Ab0p6bdp2GdADHAEcB7wX+Fj+hSS9StJXyN7T90bE8/202WrEyZr1ZjpZQva3EfFiRLwUET+MiM6IWBkRL0fEU8DF7OoYSr4YEZsj4lmyDub0al80Iq6KiB0R8TJZEvjWQX57OwP4x4jYluL8DHBmWvcKWWf9hoh4JY2lC2AnsA9wlKS9ImJTRDw6iNc2s+a6NCK2pD7oP8m+tJ0BXBURD6b+ZRHwDkmTq9znloi4LCJ6IuJXZP3IG4DDSv1jP/Vvi4h70mv/XXrtScD7gU0R8bW07weBbwKn5ureGhH/HRG/iYiXyvZ7LzBF0iHAO4ErgQmS9iPrm7+ftvsIcHtE3J72sxJYDZwkqQ14H3BB6u+3AZcAc3KvsxdwA1mi94GI+GWV75vVgJM1680k4OcR0ZMvlHSopBvTYfIXgK+TfVPM25xb/jlZ0tcvSaMkLZH0aNr3prSqfP9IuiOdPu2WdEaF3R2WXrtSHP8CdAJ3SnpM0kKAiOgELiBLEreldlYVu5kVyi9yy78E9qOsT4iIbuAZsrMG1dhc9vzjZGca7pe0VtJHASR9Mtc3fblS/fTaz6aY3gD8Xjo1+Zyk58gSy9dVqpteq7T/P0yJ42qyxOydZMnZ/wAnsHuy9gbgtLLX+QPSF1eyZGxrbt2/kx3pKzkCmA18JiJ+XeV7ZjXStEGSVnibgddLGl2WsP0zEMDvRsQzkk4BvlhWd1Ju+fXAll5eI8qe/1+yzuA9ZInaAcB2sg5x94oR7+sn/i1kHdDa8jgiYgfZqdAF6VTD9yT9KCLuiojrgesljSXrrC5k1xE5Mxu+Sn0C8NvTj4cATwAvpuLXAC+k5dexu936q4j4BfCnaV9/AHxX0j0R8U/AP1V4/d/2i+mo18Epps3A9yPij/uI/bevHRGVxvF+H3g32enLH6XnJ5KdIbknbbMZuDYi/rS8sqTxwMvAuPIv6DnrgS8Bd0h6d0QM+jJMNnA+sma9uR/YCiyRtK+kV0s6gWzMRDfwXBoD8bcV6p4raaKkg8nGg/U28PZJ4I255/uTdRjPkHWalTq8at0A/L2k10oaB/wD2VFAJL0/DQ4WWce8E9gp6UhJ71Y2EeEl4FdpnZkNf9cDfyLp2PQ3/k/AqjTc4SmypO0j6Qj/R8nG3PZK0mmSJqan28kSqr76i5Mk/YGkvcnGrq2KiM1kY4PfLOnMNEFgL0lvl/SWAbTt+8BZwLp01KuDbLzZxtQ2yPq/D0g6MbXx1comfU2MiK1k4+8ukjQ2jU17k6TdhrhExA1kffp3JfX5/lhtOVmziiJiJ/ABskPfj5MNyP8/ZGO/3gY8TzZY9ZYK1a8n+8N/LD16u5DtP5MlVM9J+hvgGrLTFE8A64D7htCEz5GdGvgpsIZsskIpjinAd8mSznuByyOig2y82hLgabLTKIeSdUxmNsxFxF3A/0s2HmwrWTKWH5P1p2RfPp8BjiY7ldiXtwOrJHUDK4DzI2JjH9tfD3yK7PTn8WSnOktH+t+bYtlC1vdcSNYfVet/gDHsOoq2juwLZ+k5KTGcTdanPUV2pO1v2ZUHnAXsnepuB24mO0W6m4hYBvwjcPcAxvvZECkbV21WG5I2AR+LiO82OxYzsyKQdDXQFRF7zIw3q4aPrJmZmZkVmJM1MzMzswLzaVAzMzOzAvORNTMzM7MCG7bXWRs3blxMnjy5qm1ffPFF9t133/oG1CCt0pZWaQe4LUP1wAMPPB0Rr+1/y5Gpmr5uOH4GHXNjOObGqCbmofR1wzZZmzx5MqtXr65q246ODtrb2+sbUIO0SltapR3gtgyVpJ/3v9XIVU1fNxw/g465MRxzY1QT81D6Op8GNTMzMyswJ2tmZoCkSZK+J2l9uv/i+an80+leuA+lx0m5OoskdUraIOnEXPnxktakdZemu2UgaR9JN6XyVb6oqJlVw8mamVmmB1gQEW8BZpDdNu2otO6SiDg2PW4HSOvmkF3tfhZwuaRRafsrgPlkd8uYktYDzAO2R8QRwCVkV6o3M+uTkzUzMyAitkbEg2l5B9mNqyf0UWU2cGNEvJxuM9QJTE83xR4bEfdGdm2ka4BTcnWWpeWbgZmlo25mZr0ZthMMzMzqJZ2ePA5YBZwAnCfpLLL7zS6IiO1kiVz+/rVdqeyVtFxeTvq5GSAieiQ9DxxCdj/a/OvPJzsyR1tbGx0dHX3G293d3e82ReOYG8MxN0a9Yx4RydqaJ57n7IW31Xy/m5acXPN9mllzSdqP7GbfF0TEC5KuAD4LRPp5EfBRoNIRseijnH7W7SqIWAosBZg2bVr0N8usVWfPDcXkOvT5C6bu5C/e317z/daTPxuNUe+YfRrUzCyRtBdZonZdRNwCEBFPRsTOiPgN8BVgetq8C5iUqz4R2JLKJ1Yo362OpNHAAcCz9WmNmbUKJ2tmZkAaO3YlsD4iLs6Vj89t9iHg4bS8ApiTZngeTjaR4P6I2ArskDQj7fMs4NZcnblp+VTg7vA9/8ysHyPiNKiZWRVOAM4E1kh6KJV9Ejhd0rFkpys3AX8GEBFrJS0H1pHNJD03InameucAVwNjgDvSA7Jk8FpJnWRH1ObUtUVm1hKcrJmZARHxQyqPKbu9jzqLgcUVylcDx1Qofwk4bQhhmtkI5NOgZmZmZgXmZM3MzMyswKpO1iSNkvRjSd9Ozw+WtFLSI+nnQbltfQsWMzMzsxoYyJG188mu6F2yELgrIqYAd6XnvgWLmZmZWQ1VlaxJmgicDHw1V5y/bcoydr+dim/BYmZmZlYD1c4G/QLwcWD/XFlbup4QEbFV0qGpvDC3YPltoGNgwdSeqrYdiGbcDmM43oajklZpB7gtZmZWX/0ma5LeD2yLiAcktVexz8LcgqXksutu5aI1tb9KyaYzqnv9WhqOt+GopFXaAW6LmZnVVzUZzAnAByWdBLwaGCvp68CTksano2rjgW1p+6HcgqXLt2AxMzMz26XfMWsRsSgiJkbEZLKJA3dHxEfY/bYpc9n9diq+BYuZmZlZDQzl3OASYLmkecDjpKty+xYsZmZmZrUzoGQtIjqAjrT8DDCzl+18CxYzMzOzGvAdDMzMzMwKzMmamZmZWYE5WTMzMzMrMCdrZmZmZgXmZM3MzMyswJysmZmZmRWYkzUzMzOzAnOyZmZmZlZgTtbMzMzMCszJmpkZIGmSpO9JWi9praTzU/nBklZKeiT9PChXZ5GkTkkbJJ2YKz9e0pq07tJ0P2TSPZNvSuWrJE1ueEPNbNhxsmZmlukBFkTEW4AZwLmSjgIWAndFxBTgrvSctG4OcDQwC7hc0qi0ryuA+cCU9JiVyucB2yPiCOAS4MJGNMzMhjcna2ZmQERsjYgH0/IOYD0wAZgNLEubLQNOScuzgRsj4uWI2Ah0AtMljQfGRsS9ERHANWV1Svu6GZhZOupmZtabAd3I3cxsJEinJ48DVgFtEbEVsoRO0qFpswnAfblqXanslbRcXl6qszntq0fS88AhwNNlrz+f7MgcbW1tdHR09Blvd3d3v9sUTb1jXjC1p+b7bBuD3+cGcMx7crJmZpYjaT/gm8AFEfFCHwe+Kq2IPsr7qrN7QcRSYCnAtGnTor29vc+YOzo66G+boql3zGcvvK3m+1wwtYcP+32uO8e8J58GNTNLJO1FlqhdFxG3pOIn06lN0s9tqbwLmJSrPhHYksonVijfrY6k0cABwLO1b4mZtRIna2ZmQBo7diWwPiIuzq1aAcxNy3OBW3Plc9IMz8PJJhLcn06Z7pA0I+3zrLI6pX2dCtydxrWZmfXKp0HNzDInAGcCayQ9lMo+CSwBlkuaBzwOnAYQEWslLQfWkc0kPTcidqZ65wBXA2OAO9IDsmTwWkmdZEfU5tS5TWbWApysmZkBEfFDKo8pA5jZS53FwOIK5auBYyqUv0RK9szMquXToGZmZmYF5mTNzMzMrMCcrJmZmZkVmJM1MzMzswJzsmZmZmZWYE7WzMzMzArMyZqZmZlZgfWbrEl6taT7Jf1E0lpJn0nlB0taKemR9POgXJ1FkjolbZB0Yq78eElr0rpL09W9SVcAvymVr0o3UTYzMzMb8ao5svYy8O6IeCtwLDBL0gxgIXBXREwB7krPkXQU2VW5jwZmAZdLGpX2dQUwn+y2LFPSeoB5wPaIOAK4BLhw6E0zMzMzG/76TdYi052e7pUeAcwGlqXyZcApaXk2cGNEvBwRG4FOYHq6AfLYiLg33QvvmrI6pX3dDMwsHXUzMzMzG8mqut1UOjL2AHAE8KWIWCWpLd2wmIjYKunQtPkE4L5c9a5U9kpaLi8v1dmc9tUj6XngEODpsjjmkx2Zo62tjY6Ojqoa2TYGFkztqWrbgaj29Wupu7u7Ka9ba63SDnBbzMysvqpK1tLNiY+VdCDwLUl73PMup9IRseijvK865XEsBZYCTJs2Ldrb2/sIY5fLrruVi9bU/jaom86o7vVrqaOjg2rbXWSt0g5wW8zMrL4GNBs0Ip4DOsjGmj2ZTm2Sfm5Lm3UBk3LVJgJbUvnECuW71ZE0GjgAeHYgsZmZmZm1ompmg742HVFD0hjgPcDPgBXA3LTZXODWtLwCmJNmeB5ONpHg/nTKdIekGWk82llldUr7OhW4O41rMzMzMxvRqjk3OB5YlsatvQpYHhHflnQvsFzSPOBx4DSAiFgraTmwDugBzk2nUQHOAa4GxgB3pAfAlcC1kjrJjqjNqUXjzMzMzIa7fpO1iPgpcFyF8meAmb3UWQwsrlC+GthjvFtEvERK9szMzMxsF9/BwMzMzKzAnKyZmZmZFZiTNTMzM7MCc7JmZmZmVmBO1szMEklXSdom6eFc2aclPSHpofQ4KbdukaROSRsknZgrP17SmrTu0tLt89IljW5K5askTW5oA81sWHKyZma2y9VkF/0ud0lEHJsetwNIOorsMkNHpzqXp0scAVxBdmu8KelR2uc8YHtEHAFcAlxYr4aYWetwsmZmlkTEPVR/95TZwI0R8XJEbAQ6genpji5jI+LedHHva4BTcnWWpeWbgZmlo25mZr2p/Q0zzcxaz3mSzgJWAwsiYjswAbgvt01XKnslLZeXk35uBoiIHknPA4cAT+dfTNJ8siNztLW10dHR0Wdw3d3d/W5TNPWOecHUnprvs20Mfp8bwDHvycmamVnfrgA+C0T6eRHwUaDSEbHoo5x+1u0qiFgKLAWYNm1atLe39xlgR0cH/W1TNPWO+eyFt9V8nwum9vBhv89155j35NOgZmZ9iIgnI2JnRPwG+AowPa3qAiblNp0IbEnlEyuU71ZH0mjgAKo/7WpmI5STNTOzPqQxaCUfAkozRVcAc9IMz8PJJhLcHxFbgR2SZqTxaGcBt+bqzE3LpwJ3p3FtZma98mlQM7NE0g1AOzBOUhfwKaBd0rFkpys3AX8GEBFrJS0H1gE9wLkRsTPt6hyymaVjgDvSA+BK4FpJnWRH1ObUvVFmNuw5WTMzSyLi9ArFV/ax/WJgcYXy1cAxFcpfAk4bSoxmNvL4NKiZmZlZgTlZMzMzMyswJ2tmZmZmBeZkzczMzKzAnKyZmZmZFZiTNTMzM7MCc7JmZmZmVmBO1szMzMwKzMmamZmZWYE5WTMzMzMrMCdrZmZmZgXWb7ImaZKk70laL2mtpPNT+cGSVkp6JP08KFdnkaROSRsknZgrP17SmrTuUklK5ftIuimVr5I0uQ5tNTMzMxt2qjmy1gMsiIi3ADOAcyUdBSwE7oqIKcBd6Tlp3RzgaGAWcLmkUWlfVwDzgSnpMSuVzwO2R8QRwCXAhTVom5mZmdmw12+yFhFbI+LBtLwDWA9MAGYDy9Jmy4BT0vJs4MaIeDkiNgKdwHRJ44GxEXFvRARwTVmd0r5uBmaWjrqZmZmZjWSjB7JxOj15HLAKaIuIrZAldJIOTZtNAO7LVetKZa+k5fLyUp3NaV89kp4HDgGeLnv9+WRH5mhra6Ojo6OquNvGwIKpPVVtOxDVvn4tdXd3N+V1a61V2gFui5mZ1VfVyZqk/YBvAhdExAt9HPiqtCL6KO+rzu4FEUuBpQDTpk2L9vb2fqLOXHbdrVy0ZkB5aVU2nVHd69dSR0cH1ba7yFqlHeC2mJlZfVU1G1TSXmSJ2nURcUsqfjKd2iT93JbKu4BJueoTgS2pfGKF8t3qSBoNHAA8O9DGmJmZmbWaamaDCrgSWB8RF+dWrQDmpuW5wK258jlphufhZBMJ7k+nTHdImpH2eVZZndK+TgXuTuPazMzMzEa0as4NngCcCayR9FAq+ySwBFguaR7wOHAaQESslbQcWEc2k/TciNiZ6p0DXA2MAe5ID8iSwWsldZIdUZsztGaZmZmZtYZ+k7WI+CGVx5QBzOylzmJgcYXy1cAxFcpfIiV7ZmZmZraL72BgZpZIukrSNkkP58p8AXAzayona2Zmu1zNrot1l/gC4GbWVE7WzMySiLiHPWei+wLgZtZUtb/4mJlZayn8BcCH48WM6x1zPS6E3jamORdDHwp/Nhqj3jE7WTMzG5zCXAB8OF7MuN4xn73wtprvc8HUHj7s97nuHPOefBrUzKxvvgC4mTWVkzUzs775AuBm1lQ+DWpmlki6AWgHxknqAj6FLwBuZk3mZM3MLImI03tZ5QuAm1nT+DSomZmZWYE5WTMzMzMrMCdrZmZmZgXmZM3MzMyswJysmZmZmRWYkzUzMzOzAnOyZmZmZlZgTtbMzMzMCszJmpmZmVmBOVkzMzMzKzAna2ZmZmYF5mTNzMzMrMCcrJmZmZkVmJM1MzMzswJzsmZmZmZWYP0ma5KukrRN0sO5soMlrZT0SPp5UG7dIkmdkjZIOjFXfrykNWndpZKUyveRdFMqXyVpco3baGZmZjZsVXNk7WpgVlnZQuCuiJgC3JWeI+koYA5wdKpzuaRRqc4VwHxgSnqU9jkP2B4RRwCXABcOtjFmZmZmrabfZC0i7gGeLSueDSxLy8uAU3LlN0bEyxGxEegEpksaD4yNiHsjIoBryuqU9nUzMLN01M3MzMxspBs9yHptEbEVICK2Sjo0lU8A7stt15XKXknL5eWlOpvTvnokPQ8cAjxd/qKS5pMdnaOtrY2Ojo7qgh0DC6b2VLXtQFT7+rXU3d3dlNettVZpB7gtZmZWX4NN1npT6YhY9FHeV509CyOWAksBpk2bFu3t7VUFddl1t3LRmlo3FTadUd3r11JHRwfVtrvIWqUd4LaY2dBNXnhbXfZ79ax967Jfa6zBzgZ9Mp3aJP3clsq7gEm57SYCW1L5xArlu9WRNBo4gD1Pu5qZNZWkTWmS1EOSVqeymk22MjPrzWCTtRXA3LQ8F7g1Vz4nzfA8nGwiwf3plOkOSTNSx3RWWZ3Svk4F7k7j2szMiuZdEXFsRExLz2s52crMrKJqLt1xA3AvcKSkLknzgCXAH0t6BPjj9JyIWAssB9YB3wHOjYidaVfnAF8lm3TwKHBHKr8SOERSJ/DXpM7OzGwYqOVkKzOzivodyBURp/eyamYv2y8GFlcoXw0cU6H8JeC0/uIwM2uyAO6UFMC/pzG0tZxs9VsDnUw1HCeG1DvmekwqaxtTv4ll9YgX/NlolHrHXPtR92ZmremEiNiSErKVkn7Wx7aDmWy1q2CAk6mG48SQesd8dh0G7C+Y2sOH6xRzPeKFbIKBPxv1V++YfbspM7MqRMSW9HMb8C1gOrWdbGVmVpGPrJmZ9UPSvsCrImJHWn4v8I/smiC1hD0nW10v6WLgMHZNttopaYekGcAqsslWlzW2NTYU9brEhllfnKyZmfWvDfhWusrGaOD6iPiOpB8By9PEq8dJ428jYq2k0mSrHvacbHU1MIZsotUdmJn1wcmamVk/IuIx4K0Vyp+hRpOtzMx64zFrZmZmZgXmZM3MzMyswJysmZmZmRWYkzUzMzOzAnOyZmZmZlZgTtbMzMzMCsyX7jAzs37V+mKwC6b21O0WS2atxkfWzMzMzArMR9bMzMxa1Jonnq/LEcxNS06u+T6tdz6yZmZmZlZgTtbMzMzMCszJmpmZmVmBOVkzMzMzKzAna2ZmZmYF5mTNzMzMrMCcrJmZmZkVmJM1MzMzswJzsmZmZmZWYL6DgZlZi6j1/TvNrBicrNmQDeYfRDU3cfbtTMzMiqleXwzc71dWmNOgkmZJ2iCpU9LCZsdjZlYv7u/MbCAKcWRN0ijgS8AfA13AjyStiIh1zY2stfgUiVnzub8z693khbdVdeZloIb7EbtCJGvAdKAzIh4DkHQjMBtw5zWC+TC7tSj3d2YNNtz/nygiGvJCfQYhnQrMioiPpednAr8XEeeVbTcfmJ+eHglsqPIlxgFP1yjcZmuVtrRKO8BtGao3RMRrG/yaTVNNfzeIvm44fgYdc2M45saoJuZB93VFObKmCmV7ZJERsRRYOuCdS6sjYtpgAiuaVmlLq7QD3BYbsH77u4H2dcPx9+aYG8MxN0a9Yy7KBIMuYFLu+URgS5NiMTOrJ/d3ZjYgRUnWfgRMkXS4pL2BOcCKJsdkZlYP7u/MbEAKcRo0InoknQf8FzAKuCoi1tbwJQZ86rTAWqUtrdIOcFtsAOrU3w3H35tjbgzH3Bh1jbkQEwzMzMzMrLKinAY1MzMzswqcrJmZmZkVWMsna0W/rYukqyRtk/RwruxgSSslPZJ+HpRbtyi1ZYOkE3Plx0tak9ZdKqnS5QHq2Y5Jkr4nab2ktZLOH8ZtebWk+yX9JLXlM8O1LSmGUZJ+LOnbw7kdVlnR+zgYXP9QBAP52ykKSQdKulnSz9L7/Y6ixy3pr9Ln4mFJN6Q+uFAx1+p/9aBFRMs+yAbvPgq8Edgb+AlwVLPjKovxncDbgIdzZZ8HFqblhcCFafmo1IZ9gMNT20aldfcD7yC7htMdwPsa3I7xwNvS8v7A/6Z4h2NbBOyXlvcCVgEzhmNbUgx/DVwPfHu4fr786PV3W/g+LsU5oP6hKI9q/3aK9ACWAR9Ly3sDBxY5bmACsBEYk54vB84uWszU6H/1YB+tfmTtt7d1iYhfA6XbuhRGRNwDPFtWPJvsD47085Rc+Y0R8XJEbAQ6gemSxgNjI+LeyD4p1+TqNEREbI2IB9PyDmA92R/hcGxLRER3erpXegTDsC2SJgInA1/NFQ+7dlivCt/HwaD6h6Yb4N9OIUgaS5ZUXAkQEb+OiOcoeNxkV6YYI2k08Bqy6w4WKuZa/K8eyuu3erI2Adice96VyoquLSK2QtbJAYem8t7aMyEtl5c3haTJwHFkR6SGZVvS6Y+HgG3AyogYrm35AvBx4De5suHYDqts2PVxVfYPRfAFqv/bKYo3Ak8BX0unb78qaV8KHHdEPAH8K/A4sBV4PiLupMAx5wy0Lx20Vk/WqrqN1TDSW3sK005J+wHfBC6IiBf62rRCWWHaEhE7I+JYsqvLT5d0TB+bF7Itkt4PbIuIB6qtUqGs6e2wPg2r380A+oemGsTfTlGMJjtVd0VEHAe8SHZ6rrDSOK/ZZKcLDwP2lfSR5kY1ZDX/u2z1ZG243tblyXTqifRzWyrvrT1dabm8vKEk7UXWEV8XEbek4mHZlpJ0CqEDmMXwa8sJwAclbSI7PfZuSV9n+LXDejds+rgB9g/NNtC/naLoArrSmQCAm8mStyLH/R5gY0Q8FRGvALcAv0+xYy4ZaF86aK2erA3X27qsAOam5bnArbnyOZL2kXQ4MAW4Px1+3SFpRpqld1auTkOk170SWB8RF+dWDce2vFbSgWl5DFln8jOGWVsiYlFETIyIyWSf/bsj4iPDrR3Wp2HRxw2if2iqQfztFEJE/ALYLOnIVDQTWEex434cmCHpNelzMpNsTGORYy4ZUF86pFeq1UyJoj6Ak8hmHj0K/F2z46kQ3w1k5+lfIcvG5wGHAHcBj6SfB+e2/7vUlg3kZuQB04CH07ovku5O0cB2/AHZYd6fAg+lx0nDtC2/C/w4teVh4B9S+bBrSy6OdnbNaBu27fCj4u+20H1cinHA/UNRHtX+7RTlARwLrE7v9X8ABxU9buAzZF+IHwauJZtFWaiYqdH/6sE+fLspMzMzswJr9dOgZmZmZsOakzUzMzOzAnOyZmZmZlZgTtbMzMzMCszJmpmZmVmBOVkzMzMzKzAna2ZmZmYF9v8BabypwGUDgNIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_attributes.hist(figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['workclass', 'education', 'marital-status', 'occupation',\n",
      "       'relationship', 'race', 'gender', 'native-country', 'income'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "cat_attributes = df.select_dtypes(include=['object'])\n",
    "print(cat_attributes.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x3456 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(24, 48))\n",
    "\n",
    "plt.subplot(411)\n",
    "sns.countplot(y='workclass', hue='income', data = cat_attributes)\n",
    "plt.title('Income vs Work Class')\n",
    "\n",
    "plt.subplot(412)\n",
    "sns.countplot(y='occupation', hue='income', data = cat_attributes)\n",
    "plt.title('Income vs Occupation')\n",
    "\n",
    "plt.subplot(413)\n",
    "sns.countplot(y='education', hue='income', data=cat_attributes)\n",
    "plt.title('Income vs Education')\n",
    "\n",
    "plt.subplot(414)\n",
    "sns.countplot(y='marital-status', hue='income', data=cat_attributes)\n",
    "plt.title('Income vs Marital Status')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<=50K    34014\n",
       ">50K     11208\n",
       "Name: income, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['income'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<=50K    0.752156\n",
       ">50K     0.247844\n",
       "Name: income, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['income'].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      0.675048\n",
       "Female    0.324952\n",
       "Name: gender, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentage of working men and women\n",
    "\n",
    "df['gender'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     low income\n",
       "1     low income\n",
       "2    high income\n",
       "3    high income\n",
       "5     low income\n",
       "Name: income_category, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['income_category'] = \"null\"\n",
    "df.loc[df['income'] == '>50K', ['income_category']] = 'high income'\n",
    "df.loc[df['income'] == '<=50K', ['income_category']] = 'low income'\n",
    "df.loc[:, 'income_category'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>income_category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income</th>\n",
       "      <th>race</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">&lt;=50K</th>\n",
       "      <th>Amer-Indian-Eskimo</th>\n",
       "      <td>382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian-Pac-Islander</th>\n",
       "      <td>934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>3694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>28696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">&gt;50K</th>\n",
       "      <th>Amer-Indian-Eskimo</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian-Pac-Islander</th>\n",
       "      <td>369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>10207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           income_category\n",
       "income race                               \n",
       "<=50K  Amer-Indian-Eskimo              382\n",
       "       Asian-Pac-Islander              934\n",
       "       Black                          3694\n",
       "       Other                           308\n",
       "       White                         28696\n",
       ">50K   Amer-Indian-Eskimo               53\n",
       "       Asian-Pac-Islander              369\n",
       "       Black                           534\n",
       "       Other                            45\n",
       "       White                         10207"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now I am interested in looking at the racewise distribution of income.\n",
    "\n",
    "racewise_income_dist = df.pivot_table(values=['income_category'], \n",
    "                                      index=['income', 'race'], \n",
    "                                      aggfunc = 'count')\n",
    "racewise_income_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>income_category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">&lt;=50K</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Amer-Indian-Eskimo</th>\n",
       "      <th>Female</th>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Asian-Pac-Islander</th>\n",
       "      <th>Female</th>\n",
       "      <td>371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Black</th>\n",
       "      <th>Female</th>\n",
       "      <td>1958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>1736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Other</th>\n",
       "      <th>Female</th>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">White</th>\n",
       "      <th>Female</th>\n",
       "      <td>10428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>18268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">&gt;50K</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Amer-Indian-Eskimo</th>\n",
       "      <th>Female</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Asian-Pac-Islander</th>\n",
       "      <th>Female</th>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Black</th>\n",
       "      <th>Female</th>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Other</th>\n",
       "      <th>Female</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">White</th>\n",
       "      <th>Female</th>\n",
       "      <td>1455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>8752</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  income_category\n",
       "income race               gender                 \n",
       "<=50K  Amer-Indian-Eskimo Female              152\n",
       "                          Male                230\n",
       "       Asian-Pac-Islander Female              371\n",
       "                          Male                563\n",
       "       Black              Female             1958\n",
       "                          Male               1736\n",
       "       Other              Female              117\n",
       "                          Male                191\n",
       "       White              Female            10428\n",
       "                          Male              18268\n",
       ">50K   Amer-Indian-Eskimo Female               14\n",
       "                          Male                 39\n",
       "       Asian-Pac-Islander Female               65\n",
       "                          Male                304\n",
       "       Black              Female              126\n",
       "                          Male                408\n",
       "       Other              Female                9\n",
       "                          Male                 36\n",
       "       White              Female             1455\n",
       "                          Male               8752"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let us also look into the racewise gender distibution of income\n",
    "\n",
    "racewise_income_dist = df.pivot_table(values=['income_category'], \n",
    "                                      index=['income', 'race', 'gender'], \n",
    "                                      aggfunc = 'count')\n",
    "racewise_income_dist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>From the above table, you can see that the difference in the number of men and women earning salary above 50,000 is huge across all races. Interestingly, more black females earn a salary of USD 50,000 or less compared to black men. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workclass</th>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Federal-gov</th>\n",
       "      <th>Female</th>\n",
       "      <td>445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Local-gov</th>\n",
       "      <th>Female</th>\n",
       "      <td>1244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>1856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Private</th>\n",
       "      <th>Female</th>\n",
       "      <td>11428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>21879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Self-emp-inc</th>\n",
       "      <th>Female</th>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>1447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Self-emp-not-inc</th>\n",
       "      <th>Female</th>\n",
       "      <td>617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>3179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">State-gov</th>\n",
       "      <th>Female</th>\n",
       "      <td>755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>1191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Without-pay</th>\n",
       "      <th>Female</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         income\n",
       "workclass        gender        \n",
       "Federal-gov      Female     445\n",
       "                 Male       961\n",
       "Local-gov        Female    1244\n",
       "                 Male      1856\n",
       "Private          Female   11428\n",
       "                 Male     21879\n",
       "Self-emp-inc     Female     199\n",
       "                 Male      1447\n",
       "Self-emp-not-inc Female     617\n",
       "                 Male      3179\n",
       "State-gov        Female     755\n",
       "                 Male      1191\n",
       "Without-pay      Female       7\n",
       "                 Male        14"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(values=['income'], index=['workclass', 'gender'], aggfunc='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Private sector provides most jobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>income</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relationship</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Husband</th>\n",
       "      <td>10159</td>\n",
       "      <td>29.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Not-in-family</th>\n",
       "      <td>10474</td>\n",
       "      <td>30.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other-relative</th>\n",
       "      <td>1299</td>\n",
       "      <td>3.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Own-child</th>\n",
       "      <td>6521</td>\n",
       "      <td>19.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unmarried</th>\n",
       "      <td>4486</td>\n",
       "      <td>13.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wife</th>\n",
       "      <td>1075</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                income  percent\n",
       "relationship                   \n",
       "Husband          10159    29.87\n",
       "Not-in-family    10474    30.79\n",
       "Other-relative    1299     3.82\n",
       "Own-child         6521    19.17\n",
       "Unmarried         4486    13.19\n",
       "Wife              1075     3.16"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Relationship-wise salary distribution for low income group\n",
    "\n",
    "relationship_wise_low_income = df.loc[df['income'] == '<=50K', :].pivot_table(values=['income'], index=['relationship'], aggfunc='count')\n",
    "relationship_wise_low_income['percent'] = round(relationship_wise_low_income['income'] * 100 / relationship_wise_low_income['income'].sum(), 2)\n",
    "relationship_wise_low_income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>income</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relationship</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Husband</th>\n",
       "      <td>8507</td>\n",
       "      <td>75.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Not-in-family</th>\n",
       "      <td>1228</td>\n",
       "      <td>10.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other-relative</th>\n",
       "      <td>50</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Own-child</th>\n",
       "      <td>105</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unmarried</th>\n",
       "      <td>302</td>\n",
       "      <td>2.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wife</th>\n",
       "      <td>1016</td>\n",
       "      <td>9.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                income  percent\n",
       "relationship                   \n",
       "Husband           8507    75.90\n",
       "Not-in-family     1228    10.96\n",
       "Other-relative      50     0.45\n",
       "Own-child          105     0.94\n",
       "Unmarried          302     2.69\n",
       "Wife              1016     9.06"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Relationship-wise salary distribution for high income group\n",
    "\n",
    "relationship_wise_high_income = df.loc[df['income'] == '>50K', :].pivot_table(values=['income'], index=['relationship'], aggfunc='count')\n",
    "relationship_wise_high_income['percent'] = round(relationship_wise_high_income['income'] * 100 / relationship_wise_high_income['income'].sum(), 2)\n",
    "relationship_wise_high_income"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>In the high income group, about 75% of people are husbands whereas in low income group, the percentage share is quite well distribted between husbands, own childs and people who are not a part of any family."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting distribution and box plots for the number of working hours for females.\n",
    "working_hours_female = df.loc[df['gender'] == 'Female', ['hours-per-week']]\n",
    "\n",
    "# Distribution plot\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.distplot(working_hours_female['hours-per-week'])\n",
    "plt.show()\n",
    "\n",
    "# Boxplot\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(working_hours_female['hours-per-week'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting distribution and box plots for the number of working hours for females.\n",
    "working_hours_male = df.loc[df['gender'] == 'Male', ['hours-per-week']]\n",
    "\n",
    "# Distribution plot\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.distplot(working_hours_male['hours-per-week'])\n",
    "plt.show()\n",
    "\n",
    "# Boxplot\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(working_hours_male['hours-per-week'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> There are lot of outliers in the 'hours-per-week' data both for males and females."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='income', ylabel='hours-per-week'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pU6ZIYjoNNCc3NzdqjfQ0bdo0ZWRkpNV0Wqr7Dc2CBQt01113adCgQTr77LP1xhtvxPz4UaNGqbq6WgMHDtS9996rCy644KD7nHnmmZo+fbr+4R/+QQMHDtTIkSNVWVkZd3ZzzsV9El/y8/NdSUmJ7xhQ3b/4ampqGurMzEwtW7bMYyIACL/y8nLdcsstDfUTTzyhvLw8j4mQLtauXRsYhvnYNi/Mmv79SJKZlTrn8pu7P2uokRCNm+nmagDAwfLy8pSbm6uKigrl5ubSTMObVG5+w4AlH0iIprs3sJsDAMRm6tSp6ty5s6ZOneo7CoBWoqFGQgwaNChqDQAAcKSioUZCNL3aV9MaANC86dOna9euXQ1XegPaSiq/jy6ZWvP3QkONhKjfIL3eeeed5ykJAKSO8vJyVVRUSJIqKipUXl7uNxDSRocOHVRVVUVT3YRzTlVVVerQoUOLHsdCVyTExx9/HKg/+ugjT0kQNlVVVbr//vt13333Ba5WBUAHTaWnT5+up556yk8YpJW+fftqw4YN2rJli+8oodOhQwf17du3RY+hoUZCrF+/PmqN9FVUVKSysjLNnTtXP/7xj33HAUKlfjp9qBpIlnbt2qlfv36+YxwxWPKBhOjYsWOg7tSpk6ckCJOqqiotXrxYzjktXrxYVVVVviMBoXLssccG6i5duvgJAiAuNNRIiD179gTq3bt3e0qCMCkqKmq4lPK+ffs0d+5cz4mAcNmxY0eg3r59u58gCJWqqipNmDCBIUQKoaEGkDRLly4N1EuWLPGUBABSR+OlckgNNNQAkqbpVd+4ChwARMdSudREQw0gacrKyqLWAICgoqIi1dbWSpJqamqYUqcIGmoASdN0f1P2OwWA6JYuXarq6mpJUnV1NUvlUgQNNYCkMbOoNQAgaMSIEcrKqtvVOCsrSyNHjvScCLGgoQaQNLfffnugvuOOO/wEAULq6KOPDtRNt9FD+iksLFRGRl17lpmZqRtvvNFzIsSChhoJUVxcHLVGeho9enSgvuKKKzwlAcLphRdeCNSLFi3ylARhkZOTo1GjRsnMNGrUKK4wmyJoqAEkTVVVVcOkJSMjg3erA82on1IznUa9wsJCDRgwgOl0CqGhRsI88cQT6ty5s5544gnfURASRUVFDeumzYx3qwPNeOGFF1RcXMx0Gg1ycnI0e/ZsptMphIYaCTN9+nTt2rVL06dP9x0FIbF06VLV1NRIqtv+iXerAwCORDTUSIjy8nJVVFRIkioqKlReXu43EEJhyJAhgXro0KGekgAAkDw01EiIplNpptSQ2CYPiEVBQUHDH0CS5s2bp4KCAs2fP993FMTIS0NtZj82s/fNbI2ZPWNmHcysm5ktMbMPIx+7+siG1qmfTh+qRnpavnx51BoAcLDHH39ckvToo496ToJYtXlDbWZ9JE2QlO+c+5qkTEljJE2WtMw5d6qkZZEaQArbt29f1BpId02n0kypMW/evEDNlDo1+FrykSWpo5llSeokaaOk0ZKKIrcXSbrKTzQAiVL/hsRD1QCAoPrpdD2m1KmhzRtq59znkmZK+kxSpaQdzrlXJPV0zlVG7lMp6bjmHm9mY82sxMxKtmzZ0laxAQAAgGb5WPLRVXXT6H6SekvqbGbXx/p459xjzrl851x+jx49khUTAAAAiImPJR8jJH3inNvinDsg6TlJ35C0ycx6SVLk42YP2QAkUNNdPuqvmggAaN6tt94aqMeNG+cpCVrCx0+3zyRdYGadrO6n7cWS1kp6XlJh5D6FkrhkVAopLi6OWiM9dezYMVB36NDBUxIgnHjtRFPXXXddoB4zZoynJGgJH2uo/1vSAknvSCqLZHhM0kOSRprZh5JGRmqkiNmzZwfqRx55xFMShMm5554bqM877zxPSYBwKikpCdSlpaWekiBM6qfUTKdThznnfGdotfz8fNf0xQh+NLfVE5MW3HDDDVq/fn1DfcIJJ+gPf/iDx0RAuFx++eXauXNnQ52dna0XX3zRYyIAh2Jmpc65/OZuY0EjgKRp3Ew3VwPprnEz3VwNIDXQUANImtzc3Kg1kO6ys7Oj1gBSAw01gKSZOnVq1BpId9OmTQvU999/v58gAOJCQw0gafLy8hqm0rm5ucrLy/MbCAiZ/PzgcszBgwd7SgIgHjTUAJJq6tSp6ty5M9NpoBns8gEcGWiokRAnnHBCoD7ppJM8JUHYPPTQQ9q1a5dmzpzpOwoQOk2XfNx3331+giBUqqqqNGHCBFVVVfmOghjRUCMhmv5Pv2XLFk9JEDbl5eWSpHXr1nlOAoQPu3ygOUVFRSorK9PcuXN9R0GMaKiRELt3745aIz3dcsstgZqLFABAdFVVVVq8eLGcc1q8eDFT6hRBQw0gaeqn0/WYUgNAdEVFRaqtrZUk1dTUMKVOETTUAAAAIbF06VJVV1dLkqqrq7VkyRLPiRALGmoAAICQGDFihLKysiRJWVlZGjlypOdEiAUNNYCkabrvdP/+/T0lAYDUUFhYqIyMuvYsMzNTN954o+dEiAUNNRKiU6dOUWukpyeeeCJQP/roo56SAEBqyMnJ0ahRo2RmGjVqlHJycnxHQgxoqJEQQ4cOjVojPTV9dzrvVgeC+vbtG7VGeiosLNSAAQOYTqcQGmokhHPOdwSEUFFRUaDm3epAUNNlUU1rpKfVq1frvffe03vvvec7CmJEQ42EWLFiRaBevny5pyQIk6VLlwZq3q0OBK1cuTJqjfT04IMPSpJmzJjhOQliRUONhODCLmhOu3btAnX79u09JQHCiddONPXqq68Gts177bXXPCdCLGioASTNjh07AvW2bds8JQGA1FA/na7HlDo10FADAACERP10+lA1womGGgAAICTqL+pyqBrhREMNIGnOOOOMQD1gwABPSYBwyszMDNQ0T5gyZUqgvueeezwlQUvQUCMhiouLo9ZIT7/97W8D9Zw5czwlAcJp2bJlgbrpzjhIP8OHDw/Uw4YN85QELUFDjYQoKCiIWiM98X0BRMf/I2hq0qRJgXry5MmekqAlaKgBAABCoqSkJFC/9dZbnpKgJWioAQAAgDjQUAMAAABxoKEGAAAIifz8/EB9wQUXeEqClqChRkKwyweaw/cFEF2nTp2i1kg/M2fODNQPPfSQpyRoCRpqJMTs2bMD9SOPPOIpCcKkvLw8ag2ku5NOOilqjfRUP6VmOp06zDnnO0Or5efnu6bvhoUfzW31xDQSN910kyoqKhrq3NxcPfXUU97yAGHDayeQOsys1DmX39xtTKgBJE3jZrq5GgCAIwENNYCk6du3b9QaAIAjAQ01EiI3NzdQ9+vXz08QhEpeXl7UGgCAIwENNRJi8+bNgXrTpk2ekiBMVq5cGbUGAOBIQEONhKitrQ3UqfxmVyTO0KFDo9ZAusvIyIhaA0gN/J+LhNi7d2+g3rNnj6ckCBP+YQVE165du6g10tOkSZNUUFCgyZMn+46CGNFQA0iaFStWBOrly5d7SgKE0759+6LWSE/1WwK/9dZbnpMgVjTUAJJm9+7dUWsg3WVnZ0etkX4mTZoUqJlSp4Ys3wEAAPBlzpw5Xq/g2aNHD+3cubOhPu644zRx4sQ2z5GXl6fx48e3+fPiYE0vWMeUOjUwoQYAwJNjjjmm4fOMjAwdffTRHtMAaC0m1ACAtBWGqewPfvADffLJJ/rlL3+pwYMH+44DoBWYUAMA4NExxxyjQYMG0UxDkpSfnx+oL7jgAk9J0BI01AAAACExc+bMQP3QQw95SoKWoKEGAAAIiaqqqqg1womGGkDScNEKAGiZoqIiZWZmSpIyMzM1d+5cz4kQCxpqAElz4MCBqDUAIGjp0qWqqamRJNXU1GjJkiWeEyEWNNQAkqZDhw5RawBA0JAhQwL10KFDPSVBS9BQA0gaJtQA0DJm5jsCWiGmhtrM2jdzrFvi4wA4ktT/2vJQNQAgaPny5VFrhFOsE+rnzKzh3URm1ksSi3oARJWVlRW1BgAEjRgxouG1MisrSyNHjvScCLGItaH+T0nPmlmmmeVK+quku5MVCqnn2GOPDdRdunTxEwShMmXKlEB9zz33eEoCAKmhsLBQ1dXVkqTq6mrdeOONnhMhFjE11M65x1U3kf5PSS9IGuece6W1T2pmXcxsgZmtM7O1ZnahmXUzsyVm9mHkY9fWnh9tr+na2P3793tKgjAZPnx4oB42bJinJACQGnJycqLWCKeoDbWZ3VH/R1IHSSdIWiXpgsix1polabFzrr+kQZLWSposaZlz7lRJyyI1UsTu3buj1khPBQUFUWsAQBCvm6npcAsaj25SLzzE8ZiZ2TGSviXpJklyzu2XtN/MRksqiNytSFKxpLta+zwAAABAW4jaUDvn7m9cm1ln59yuOJ/zZElbJD1pZoMklUqaKKmnc64y8ryVZnZccw82s7GSxkrSiSeeGGcUAAAAID6xbpt3oZn9XXVLM2Rmg8zsN618zixJ50j6rXPu65J2qQXLO5xzjznn8p1z+T169GhlBAAAACAxYt3l49eSLpFUJUnOudWqW7bRGhskbXDO/XekXqC6BntTZDu++m35Nrfy/AAAAECbiflKic659U0OteoKDc65/5W03sxOjxy6WNLfJT0vqTByrFDSotacH0B4dOrUKWoNAAgqLi6OWiOcYm2o15vZNyQ5MzvKzCYpsvyjlcZLmmdm70k6W9KDkh6SNNLMPpQ0MlIjRWRnZ0etkZ5GjBgRqLlAAQDgSBTrZcvGqW6ruz6qW7LxiqR/bu2TOudWScpv5qaLW3tO+NWuXbtA3b79QVerRxpqemXEo446ylMSAEgdTKVTT6wXdvnCOXedc66nc+4459z1zrmqZIdD6ti2bVugrqri2wPSc889F6ifffZZT0kAIHVUVVVpwoQJ/CxNIbHu8nGamS0zszWReqCZTU1uNAAAgPRTVFSksrIyzZ0713cUxCjWNdSPS7pb0gFJcs69J2lMskIBAACko6qqKi1evFjOOS1evJgpdYqItaHu5Jxb2eRYdaLDADiymFnUGgAQVFRUpOrquhbrwIEDTKlTRKwN9RdmdookJ0lm9l1JlUlLBeCI4JyLWgMAgpYuXara2lpJUm1trZYsWeI5EWIRa0P9z5L+XVJ/M/tc0u2q2/kDAAAACXLSSScF6n79+nlKgpaIads859zHkkaYWWdJGc65r5IbCwAAIP2sXRu8zMf777/vKQlaItZdPj4ys3mSbpB0QnIjAQAAAKkj1iUfZ6puyUeOpJlm9rGZLUxeLAAAACA1xHqlxBrVbZlXI6lW0iZJm5MVCgAAwJc5c+aovLzcy3P37NlTmzZtaqiPP/54TZw40UuWvLw8jR8/3stzp5pYG+ovJZVJeljS41wlEQAAIPGOP/74QEPds2dPj2kQq1gb6mslDZH0T5JuMbM3JL3unFuWtGQAAAAe+J7KXnPNNdq0aZPGjRunMWO4jl4qiHWXj0WSFplZf0mXqm7bvJ9K6pi8aAAAAOnn+OOP1/HHH08znUJi3eXjz2b2kaRZkrIl3SipazKDAQAAAKkg1iUfD0l6xzlXk8wwAAAAQKqJaULtnHu7vpk2s8eSGwkAAABIHbHuQ91YfsJTAAAAACnqsA21mWWY2TcaHWL/aQAxMbOoNQAAR4LDrqF2ztWa2a8kXRipRyU9FVrM5yb0Ul2j5JwL1GxE75/v74tevXpp48aNDXXv3r29fF/wPQEASKZYl3y8YmbfMcZLOISBAwdGrZGeevToEai7d+/uKQkAAMkT6y4fd0jqLKnGzPZIMknOOXdM0pKhRcIwfRs2bJicc8rJydGsWbN8x4HC8X3x/e9/Xxs3btSdd96pK664wnccAAASLtYLuxyd7CBIffVTaZppNNajRw/16NGDZhoAcMSK9cIuZmbXm9m9kfoEMzsvudEAAACA8It1DfVvVPemxO9H6p2SHklKIgAAACCFxLqG+nzn3Dlm9q4kOee2mdlRScwFADjC+d6FJizq/w587YwUFuzGg1QWa0N9wMwyJTlJMrMekmqTlgoAcMQrLy/XqjVrVdOpm+8oXmXsr9tytPTjTZ6T+JO5e6vvCEBcYm2oZ0taKKmnmc2Q9F1JU5OWCgCQFmo6ddOe/pf5jgHPOq57yXcEIC6x7vIxz8xKJV0cOXSVc25t8mIBAAAAqSHWCbUkdZJUv+yjY3LiAAAAAKkl1m3zfiapSFI3Sd0lPWlmLPkAAABA2ot1Qn2tpK875/ZKkpk9JOkdSdOTFQwAAABIBbHuQ10hqUOjur2kjxKeBgAAAEgxsU6o90l638yWqG4N9UhJK8xstiQ55yYkKR8AAAAQarE21Asjf+oVJz4KAAAAkHpi3TavqP5zMzvHOfdO8iIBAAAAqSPWNdSNPZHwFAAAAECKask+1PUs4SkAAAAkzZkzR+Xl5b5jeFX/9U+cONFzEv/y8vI0fvx43zEOqzUN9f0JTwEAAKC6ZvLD99/Vidk1vqN4c9SBugUE+z4t8ZzEr892ZvqOELOYGmoz+6akVc65XZKyzexhSbOcc58mNR0AAEg7J2bXaMo5X/qOAc8efOcY3xFiFuuE+reSBpnZIEk/kfR7SXMlXZSsYKmEX0/V4VdUdVLl11OAb59//rkyd+9Qx3Uv+Y4CzzJ3V+nzz6t9xwBaLdaGuto558xstKTZzrnfmVlhMoOlkvLycq1as1Y1nbr5juJVxn4nSSr9eJPnJP5k7t7qOwIAAGhjsTbUX5nZ3ZKul/QtM8uU1C55sVJPTadu2tP/Mt8x4BmTNiB2ffr00f/uy+K1E+q47iX16dPTdwyg1WLdNu8a1V0t8YfOuf+V1EfSL5OWCgAAAEgRh51QR6bRTzvnRtQfc859pro11AAAAEBaO+yE2jlXI2m3mR3bBnkAAACAlBLrGuq9ksrMbImkXfUHnXMTkpIKAAAASBGxNtR/ifwBAAAA0EhMDbVzrijZQQAAAIBUFOuVEj+R5Joed86dnPBEAAAAQAqJdclHfqPPO0j6nqT0vooJAAAAoBj3oXbOVTX687lz7teShic3GgAAABB+sS75OKdRmaG6ifXRSUkEAAAApJBYl3z8qtHn1ZIqJP2feJ44csGYEkmfO+cuN7Nukv4kKbf+/M65bfE8BwAAAJBsse7yMSwJzz1R0lpJx0TqyZKWOeceMrPJkfquJDwvAAAAkDCxLvk4VtJ9kr4VOfQ3SQ8453a05knNrK+kb0uaIemOyOHRkgoinxdJKlaKNNSff/65Mr+qUvY7f/Adxa/amrqPGZl+c/hUU63PP6+WJN1yyy2qrKz0HMi/PXv2SJK+/e1ve07iV69evfTEE0/4jhE6mbu3quO6l3zH8Cpj75eSpNoOxxzmnkeuzN1bJfWUVPczdev2LP3fv3X1G8qjA7UmSWqXcdAGa2llX42pW9bnvmPEJNYlH7+XtEb//zKPGyQ9KekfW/m8v5b0UwXXYfd0zlVKknOu0syOa+6BZjZW0lhJOvHEE1v59InVpUuXhqYhndX/HXTscJTnJD4dpS5dukiStm/frj27dqp9Znq/IGa4uh8MtXu/8pzEn301pu3bt/uOETp5eXm+I4RCeXnd/xt5J/f0nMSnng3fD/xMlWojX39Gh46ek/jVUWr4mRp2sTbUpzjnvtOovt/MVrXmCc3sckmbnXOlZlbQ0sc75x6T9Jgk5efnh6JTYepUZ+LEiZKkWbNmeU4SDn369FH36kpNOedL31Hg2YPvHKP2ffr4jhE648eP9x0hFHjtDOJnKt8TqSimbfMk7TGzIfWFmX1TUmv/+fhNSVeaWYWk+ZKGm9nTkjaZWa/I+XtJ2tzK8wMAAABtJtaG+keSHjGzikgj/G+S/m9rntA5d7dzrq9zLlfSGEmvOueul/S8pMLI3QolLWrN+QEAAIC2FOuSj7WS/j9Jp0jqImmHpKskvZfALA9J+g8z+6Gkz1R3NUYAAAAg1GJtqBdJ2i7pHUkJe7ulc65Ydbt5yDlXJeniRJ0bAAAAaAuxNtR9nXOjkpoEAAAASEGxrqF+w8wGJDUJAAAAkIKiTqjNrEySi9zvB2b2saR9kkySc84NTH5EAAAAILwOt+Tj8jZJAQAAAKSoqA21c+7TtgoCAAAApKJY11ADAAAAaAYNNQAAABAHGmoAAAAgDjTUAAAAQBxoqAEAAIA40FADAAAAcaChBgAAAOJAQw0AAADEgYYaAAAAiMPhLj2OFDFnzhyVl5d7zbB69WpJUkFBgQYNGuQtR15ensaPH+/t+QEAQHphQg0AAADEgQn1EcL3RLagoCBQr169WsXFxV6yAAAAtCUaaiCJPtuZqQffOcZ3DK827a77RVjPTrWek/jz2c5Mneo7BAAgaWiogSTJy8vzHSEU9kfW9rc/KX3/Pk4V3w8AcCSjoQaSxPcynLCYOHGiJGnWrFmekwAAkBw01AAAAI343jmLXbNSD7t8AAAAAHFgQg0AANCIz6ksu2alJibUAAAAQBxoqAEAAIA40FADAAAAcaChBgAAAOJAQw0AABASTd+AyBsSUwMNNQAAQEjMmzcvUM+fP99TErQEDTUAAEBIPP7444H60Ucf9ZQELUFDDQAAAMSBhhoAAACIAw01AABASBx77LGBukuXLn6CoEVoqAEAAEJix44dgXr79u1+gqBFaKgBAABCIjc3N2qNcKKhBgAACImpU6dGrRFONNQAAAAhkZeXF7VGONFQAwAAhMS1114bqK+//npPSdASWb4DAADgy5w5c1ReXu41w+rVqyVJl156qU477TQvGfLy8jR+/Hgvz42gysrKQL1hwwZPSdASTKgBAAiBPXv2+I4AoJWYUAMA0pbvqeyECRMCdWZmph5++GFPaQC0FhNqAAA8ee+99wL1O++84ykJgHjQUAMAAABxoKEGAAAA4kBDDQCAJ127dg3U3bp185QEYdGrV69A3bdvX09J0BI01AAAeLJv375AvXfvXk9JEBbPPPNMoH766ac9JUFL0FADAODJkCFDAvXQoUM9JUFYNN0X3fc+6YgNDTUAAJ6Yme8ICJnp06dHrRFONNQAAHiyfPnyqDXST0VFRdQa4URDDQCAJ02XeLDkA927dw/UPXr08JQELUFDDQCAJ8453xEQMlu3bg3UVVVVnpKgJdq8oTazE8zsNTNba2bvm9nEyPFuZrbEzD6MfOx6uHMBAJDKVqxYEahZ8oHa2tqoNcLJx4S6WtKdzrkzJF0g6Z/N7ExJkyUtc86dKmlZpAYA4Ii1e/fuqDWA1JDV1k/onKuUVBn5/CszWyupj6TRkgoidyuSVCzprrbOBxxJ5syZ433LpfrnnzhxorcMeXl5Gj9+vLfnBwAc2byuoTazXElfl/TfknpGmu36pvu4QzxmrJmVmFnJli1b2iwrgNbZtWuXdu3apXXr1vmOAgBAUrT5hLqemWVL+rOk251zX8a6F6dz7jFJj0lSfn4+7+YAogjDVLagoEBS3RXhZs2a5TcMAABJ4GVCbWbtVNdMz3POPRc5vMnMekVu7yVps49sABLnlltuCdTjxo3zlAQAgOTxscuHSfqdpLXOuYcb3fS8pMLI54WSFrV1NrReVlZW1Brpqen6bZZ9AEHFxcVRa6QfvidSk4+u55uSbpBUZmarIsemSHpI0n+Y2Q8lfSbpex6yoZWqq6uj1gAAAEcqH7t8rJB0qAXTF7dlFgAAfJo3b16gnj9/vsaMGeMpDcKg6W/2ysvLlZeX5ykNYsWVEgEA8OTxxx8P1I8++qinJAiL6dOnR60RTjTUAAAAIVFRURG1RjjRUAMAAIRE7969o9YIJxpqAAA86d+/f6A+44wzPCVBWDjHJTZSEQ01AACefPbZZ4H6008/9ZQEYVFZWRmoN27c6CkJWoKGGgAAT5pOpJlQo3379lFrhBMNNQAAnqxevTpqjfSzb9++qDXCiYYaAABPuCgWcGSgoQYAAADiQEMNAAAAxIGGGgAAAIgDDTUAAAAQBxpqAAA8Oe644wL18ccf7ykJwqJLly6BumvXrn6CoEVoqAEA8GTnzp2B+ssvv/SUBGGxf//+QM22eamBhhoAAE/OO++8qDXSz5AhQwL10KFDPSVBS9BQAwDgSXl5edQa6cfMfEdAK9BQAwDgyYYNG6LWSD/Lly+PWiOcaKgBAPCk6RvOunXr5ikJwuKkk04K1Lm5uX6CoEVoqAEA8GTbtm2BeuvWrZ6SICzWrl0bqP/+9797SoKWoKEGAAAA4kBDDQCAJ03fgMYb0oDUREMNAIAnt99+e6C+4447/ARBaNx6662Bety4cZ6SoCVoqAEA8GT06NGB+oorrvCUBGFx3XXXBeoxY8Z4SoKWoKEGAMCTSZMmBerJkyd7SoKw+Jd/+ZdA/atf/cpTErQEDTUAAJ6UlJQE6rfeestTEoTFyy+/HKhfeOEFT0nQEjTUAAAAQBxoqAEAAIA40FADAOBJRkZG1Brp59JLLw3UvFE1NfB/LgAAntTW1katkX7uuuuuQH3nnXd6SoKWoKEGAMCTdu3aRa2RfhYtWhSoeVNiaqChBgDAkwMHDkStkX5+/etfB+qHH37YTxC0CA01AABASDjnotYIJxpqAAAAIA401AAAeMIaajTVrVu3QJ2Tk+MpCVqChhoAAE9YQ42mtm7dGqirqqo8JUFL0FADAOBJhw4dotZIP7169QrUvXv39pQELUFDDQCAJ2YWtQaQGmioAQDwZM+ePVFrpJ/KyspAvXHjRk9J0BI01AAAeNK+ffuoNdJP0zchdu/e3VMStAQNNQAAnvCmRDS1ffv2QL1t2zY/QdAiNNQAAHhSW1sbtUb6qampiVojnGioAQDwJDMzM2qN9JORkRG1RjjxXwkAAE+ysrKi1kg/XNglNdFQAwDgyb59+6LWSD9ffPFFoN6yZYunJGgJGmoAADzp1KlT1Brp54QTTohaI5xoqAEA8GTQoEGB+uyzz/YTBKFx8sknB+pTTjnFUxK0BA01AACerF69OlCvWrXKTxCExttvvx2oV65c6SkJWoKGGgAAT84777yoNdLPkCFDAvXQoUM9JUFL0FADAOBJeXl51Brpx8x8R0Ar0FADAODJhg0botZIP8uXL49aI5xoqAEA8KRXr16Bunfv3p6SICxGjBjRsB95VlaWRo4c6TkRYkFDDSBpuOIXEB2/3kdThYWFDa+VmZmZuvHGGz0nQixC99PNzEaZ2QdmVm5mk33nAdB6tbW1UWsg3W3cuDFqjfSTk5OjUaNGycw0atQorpSYIkLVUJtZpqRHJF0q6UxJ15rZmX5TAWit7OzsqDWQ7nJzc6PWSE+FhYUaMGAA0+kUEqqGWtJ5ksqdcx875/ZLmi9ptOdMiMF1110XqHkRgCRNmzYtUN9///1+ggAhNXXq1Kg10lNOTo5mz57NdDqFhK2h7iNpfaN6Q+RYAzMba2YlZlbC9e3D49Zbbw3UN998s6ckCJP8/PyGqXR2drYGDx7sOREQLnl5eQ1T6dzcXOXl5fkNBKBVwtZQN/fuDBconHvMOZfvnMvv0aNHG8VCLOqn1Eyn0di0adOUkZHBdBo4hKlTp6pz585Mp4EUZs65w9+rjZjZhZKmOecuidR3S5Jz7hfN3T8/P9+VlJS0YUIAAACkIzMrdc7lN3db2CbUb0s61cz6mdlRksZIet5zJgAAAOCQsnwHaMw5V21mt0n6q6RMSb93zr3vORYAAABwSKFqqCXJOfeSpJd85wAAAABiEbYlHwAAAEBKoaEGAAAA4kBDDQAAAMSBhhoAAACIAw01AAAAEAcaagAAACAOobpSYkuZ2RZJn/rOARxCd0lf+A4BACmG106E1UnOuR7N3ZDSDTUQZmZWcqhLlAIAmsdrJ1IRSz4AAACAONBQAwAAAHGgoQaS5zHfAQAgBfHaiZTDGmoAAAAgDkyoAQAAgDjQUANJYGYFZrbDzFZF/vys0W2jzOwDMys3s8mNjj9lZt+NfN7NzN41sx/4yA8AbSHyuvdJo9fKsyPHzcxmR14n3zOzcxo9Zmejzy8zsw/N7EQP8YEGWb4DAKnCzI6S1M45tyvGhyx3zl3e5ByZkh6RNFLSBklvm9nzzrm/N7rPsZL+Kukx59yTiUkPAG3PzLo657Yd5m4/cc4taHLsUkmnRv6cL+m3kY+Nz32xpDmS/sE591mCIgOtwoQaOAwzO8PMfiXpA0mnxXm68ySVO+c+ds7tlzRf0uhGt2dLelnSH51zv43zuQDAtxIz+6OZDTcza8HjRkua6+q8JamLmfWqv9HMhkp6XNK3nXMfJTgz0GI01EAzzKyzmf3AzFZIekLSWkkDnXPvRm7/10a/omz8Z3Kj01xoZqvN7GUzOytyrI+k9Y3usyFyrN7DklY45/41eV8dALSZ0yT9UdJtkv5uZlPMrHeT+8yILOv4VzNrHzkW7bWyvaRFkq5yzq1LYnYgZiz5AJpXKek9Sbc094LtnPvxYR7/juouUbrTzC6T9J+q+9VlcxOaxlvtvCpptJnNdM5tblVyAAgJ51yNpBclvWhmPST9QtJnZvYN59xKSXdL+l9JR6luu7y7JD2g6K+VByS9IemHkiYm9ysAYsOEGmjedyV9Lmmhmf3MzE5qfOPhJtTOuS+dczsjn78kqZ2ZdVfdlOWERqfqK2ljo3q+6tYKvmRmRyfvywOAtmFmx5rZWEnPq25i/UPVDSzknKuMLOvYJ+lJ1S2Lk6K/VtZK+j+SzjWzKW3wJQCHxYQaaIZz7hVJr5hZjqTrJS0ysy9UN7GuONyE2syOl7TJOefM7DzV/eO1StJ2SaeaWT/VNexjJH2/yXP/OrJWcKGZXRZZaw0AKcfMnpZ0oaRnJd3onPuwye29nHOVkfXVV0laE7npeUm3mdl81b0ZcYdzrrL+cc653WZ2uaTlZrbJOfe7NvhygEOioQaicM5VSZolaVakMa6J8aHflfQjM6uWtEfSGFd3FaVqM7tNdbt4ZEr6vXPu/Wae9y4ze1LSH8zsWudcbSK+HgBoY/8h6SbnXPUhbp8XWQpiklZJGhc5/pKkyySVS9ot6aAtRJ1zW81slKTXzewL59yiRIcHYsWVEgEAAIA4sIYaAAAAiAMNNQAAABAHGmoAAAAgDjTUAAAAQBxoqAEAAIA40FADQAoxszd8ZwAABLFtHgAAABAHJtQAkELMbGfkY4GZFZvZAjNbZ2bzIlebk5mda2ZvmNlqM1tpZkebWQcze9LMyszsXTMbFrnvTWb2n2b2gpl9Yma3mdkdkfu8ZWbdIvc7xcwWm1mpmS03s/7+/hYAIFy4UiIApK6vSzpL0kZJ/yXpm2a2UtKfJF3jnHvbzI5R3dU6J0qSc25ApBl+xcxOi5zna5FzdVDdlenucs593cz+VdKNkn4t6TFJ45xzH5rZ+ZJ+I2l4G32dABBqNNQAkLpWOuc2SJKZrZKUK2mHpErn3NuS5Jz7MnL7EElzIsfWmdmnkuob6tecc19J+srMdkh6IXK8TNJAM8uW9A1Jz0aG4JLUPrlfGgCkDhpqAEhd+xp9XqO613ST1NybY6yZY82dp7ZRXRs5Z4ak7c65s1udFACOYKyhBoAjyzpJvc3sXEmKrJ/OkvS6pOsix06TdKKkD2I5YWTK/YmZfS/yeDOzQckIDwCpiIYaAI4gzrn9kq6RNMfMVktaorq10b+RlGlmZapbY32Tc27foc90kOsk/TByzvcljU5scgBIXWybBwAAAMSBCTUAAAAQBxpqAAAAIA401AAAAEAcaKgBAACAONBQAwAAAHGgoQYAAADiQEMNAAAAxIGGGgAAAIjD/wMTkwL2SM0QjAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grouped boxplot for income, gender and number of hours per week.\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(data=df, x='income', y='hours-per-week', hue='gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>From the boxplot above, it can be concluded that men worked for few more hours than women in both the salary brackets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='income', ylabel='age'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(data=df, x='income', y='age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Data Prepration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Removing outliers and unnecessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'fnlwgt', 'education', 'educational-num',\n",
       "       'marital-status', 'occupation', 'relationship', 'race', 'gender',\n",
       "       'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',\n",
       "       'income', 'income_category'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of columns before deleting: 16\n",
      "Number of columns after deleting: 14\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of columns before deleting: {df.shape[1]}\")\n",
    "\n",
    "del_cols = ['fnlwgt','education']\n",
    "df.drop(labels = del_cols, axis = 1, inplace = True)\n",
    "print(f\"Number of columns after deleting: {df.shape[1]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of observation before removing: (45222, 14)\n",
      "Number of observation after removing: (45176, 14)\n"
     ]
    }
   ],
   "source": [
    "# drop rows with age 90\n",
    "print(\"Number of observation before removing:\", df.shape)\n",
    "index_age = df[df['age'] == 90].index\n",
    "df.drop(labels = index_age, axis = 0, inplace =True)\n",
    "print(\"Number of observation after removing:\", df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of observation before removing: (45176, 14)\n",
      "Number of observation after removing: (44947, 14)\n"
     ]
    }
   ],
   "source": [
    "#drop rows with capital gain = 99999\n",
    "print(\"Number of observation before removing:\",df.shape)\n",
    "index_gain = df[df['capital-gain'] == 99999].index\n",
    "df.drop(labels = index_gain,axis = 0,inplace =True)\n",
    "print(\"Number of observation after removing:\",df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('income_category', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>workclass</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Private</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Private</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Local-gov</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Private</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Private</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   workclass      marital-status         occupation   relationship   race  \\\n",
       "0    Private       Never-married  Machine-op-inspct      Own-child  Black   \n",
       "1    Private  Married-civ-spouse    Farming-fishing        Husband  White   \n",
       "2  Local-gov  Married-civ-spouse    Protective-serv        Husband  White   \n",
       "3    Private  Married-civ-spouse  Machine-op-inspct        Husband  Black   \n",
       "5    Private       Never-married      Other-service  Not-in-family  White   \n",
       "\n",
       "  gender native-country income  \n",
       "0   Male  United-States  <=50K  \n",
       "1   Male  United-States  <=50K  \n",
       "2   Male  United-States   >50K  \n",
       "3   Male  United-States   >50K  \n",
       "5   Male  United-States  <=50K  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical = df.select_dtypes(include=['object'])\n",
    "\n",
    "categorical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>10</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  educational-num  capital-gain  capital-loss  hours-per-week\n",
       "0   25                7             0             0              40\n",
       "1   38                9             0             0              50\n",
       "2   28               12             0             0              40\n",
       "3   44               10          7688             0              40\n",
       "5   34                6             0             0              30"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical = df.select_dtypes(include=['int64', 'float64'])\n",
    "\n",
    "numerical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.025420</td>\n",
       "      <td>-1.219743</td>\n",
       "      <td>-0.232520</td>\n",
       "      <td>-0.219315</td>\n",
       "      <td>-0.074097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.034670</td>\n",
       "      <td>-0.433575</td>\n",
       "      <td>-0.232520</td>\n",
       "      <td>-0.219315</td>\n",
       "      <td>0.760953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.796786</td>\n",
       "      <td>0.745676</td>\n",
       "      <td>-0.232520</td>\n",
       "      <td>-0.219315</td>\n",
       "      <td>-0.074097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.422600</td>\n",
       "      <td>-0.040492</td>\n",
       "      <td>2.767105</td>\n",
       "      <td>-0.219315</td>\n",
       "      <td>-0.074097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.339516</td>\n",
       "      <td>-1.612827</td>\n",
       "      <td>-0.232520</td>\n",
       "      <td>-0.219315</td>\n",
       "      <td>-0.909146</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  educational-num  capital-gain  capital-loss  hours-per-week\n",
       "0 -1.025420        -1.219743     -0.232520     -0.219315       -0.074097\n",
       "1 -0.034670        -0.433575     -0.232520     -0.219315        0.760953\n",
       "2 -0.796786         0.745676     -0.232520     -0.219315       -0.074097\n",
       "3  0.422600        -0.040492      2.767105     -0.219315       -0.074097\n",
       "4 -0.339516        -1.612827     -0.232520     -0.219315       -0.909146"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scaling the numerical features\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# column names are (annoyingly) lost after Scaling\n",
    "# (i.e. the dataframe is converted to a numpy ndarray)\n",
    "\n",
    "df_num = pd.DataFrame(scaler.fit_transform(numerical), \n",
    "                      columns = numerical.columns)\n",
    "\n",
    "df_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>workclass_Local-gov</th>\n",
       "      <th>workclass_Private</th>\n",
       "      <th>workclass_Self-emp-inc</th>\n",
       "      <th>workclass_Self-emp-not-inc</th>\n",
       "      <th>workclass_State-gov</th>\n",
       "      <th>workclass_Without-pay</th>\n",
       "      <th>marital-status_Married-AF-spouse</th>\n",
       "      <th>marital-status_Married-civ-spouse</th>\n",
       "      <th>marital-status_Married-spouse-absent</th>\n",
       "      <th>marital-status_Never-married</th>\n",
       "      <th>...</th>\n",
       "      <th>native-country_Puerto-Rico</th>\n",
       "      <th>native-country_Scotland</th>\n",
       "      <th>native-country_South</th>\n",
       "      <th>native-country_Taiwan</th>\n",
       "      <th>native-country_Thailand</th>\n",
       "      <th>native-country_Trinadad&amp;Tobago</th>\n",
       "      <th>native-country_United-States</th>\n",
       "      <th>native-country_Vietnam</th>\n",
       "      <th>native-country_Yugoslavia</th>\n",
       "      <th>income_&gt;50K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   workclass_Local-gov  workclass_Private  workclass_Self-emp-inc  \\\n",
       "0                  0.0                1.0                     0.0   \n",
       "1                  0.0                1.0                     0.0   \n",
       "2                  1.0                0.0                     0.0   \n",
       "3                  0.0                1.0                     0.0   \n",
       "4                  0.0                1.0                     0.0   \n",
       "\n",
       "   workclass_Self-emp-not-inc  workclass_State-gov  workclass_Without-pay  \\\n",
       "0                         0.0                  0.0                    0.0   \n",
       "1                         0.0                  0.0                    0.0   \n",
       "2                         0.0                  0.0                    0.0   \n",
       "3                         0.0                  0.0                    0.0   \n",
       "4                         0.0                  0.0                    0.0   \n",
       "\n",
       "   marital-status_Married-AF-spouse  marital-status_Married-civ-spouse  \\\n",
       "0                               0.0                                0.0   \n",
       "1                               0.0                                1.0   \n",
       "2                               0.0                                1.0   \n",
       "3                               0.0                                1.0   \n",
       "4                               0.0                                0.0   \n",
       "\n",
       "   marital-status_Married-spouse-absent  marital-status_Never-married  ...  \\\n",
       "0                                   0.0                           1.0  ...   \n",
       "1                                   0.0                           0.0  ...   \n",
       "2                                   0.0                           0.0  ...   \n",
       "3                                   0.0                           0.0  ...   \n",
       "4                                   0.0                           1.0  ...   \n",
       "\n",
       "   native-country_Puerto-Rico  native-country_Scotland  native-country_South  \\\n",
       "0                         0.0                      0.0                   0.0   \n",
       "1                         0.0                      0.0                   0.0   \n",
       "2                         0.0                      0.0                   0.0   \n",
       "3                         0.0                      0.0                   0.0   \n",
       "4                         0.0                      0.0                   0.0   \n",
       "\n",
       "   native-country_Taiwan  native-country_Thailand  \\\n",
       "0                    0.0                      0.0   \n",
       "1                    0.0                      0.0   \n",
       "2                    0.0                      0.0   \n",
       "3                    0.0                      0.0   \n",
       "4                    0.0                      0.0   \n",
       "\n",
       "   native-country_Trinadad&Tobago  native-country_United-States  \\\n",
       "0                             0.0                           1.0   \n",
       "1                             0.0                           1.0   \n",
       "2                             0.0                           1.0   \n",
       "3                             0.0                           1.0   \n",
       "4                             0.0                           1.0   \n",
       "\n",
       "   native-country_Vietnam  native-country_Yugoslavia  income_>50K  \n",
       "0                     0.0                        0.0          0.0  \n",
       "1                     0.0                        0.0          0.0  \n",
       "2                     0.0                        0.0          1.0  \n",
       "3                     0.0                        0.0          1.0  \n",
       "4                     0.0                        0.0          0.0  \n",
       "\n",
       "[5 rows x 76 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encoding the categorical features\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "encoder = OneHotEncoder(drop='first', sparse=False)\n",
    "\n",
    "# column names are (annoyingly) lost after OneHotEncoding\n",
    "# (i.e. the dataframe is converted to a numpy ndarray)\n",
    "\n",
    "df_cat = pd.DataFrame(encoder.fit_transform(categorical), \n",
    "                      columns=encoder.get_feature_names(categorical.columns))\n",
    "\n",
    "df_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>workclass_Local-gov</th>\n",
       "      <th>workclass_Private</th>\n",
       "      <th>workclass_Self-emp-inc</th>\n",
       "      <th>workclass_Self-emp-not-inc</th>\n",
       "      <th>workclass_State-gov</th>\n",
       "      <th>...</th>\n",
       "      <th>native-country_Puerto-Rico</th>\n",
       "      <th>native-country_Scotland</th>\n",
       "      <th>native-country_South</th>\n",
       "      <th>native-country_Taiwan</th>\n",
       "      <th>native-country_Thailand</th>\n",
       "      <th>native-country_Trinadad&amp;Tobago</th>\n",
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       "      <th>income_&gt;50K</th>\n",
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       "      <td>-1.219743</td>\n",
       "      <td>-0.232520</td>\n",
       "      <td>-0.219315</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.034670</td>\n",
       "      <td>-0.433575</td>\n",
       "      <td>-0.232520</td>\n",
       "      <td>-0.219315</td>\n",
       "      <td>0.760953</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.796786</td>\n",
       "      <td>0.745676</td>\n",
       "      <td>-0.232520</td>\n",
       "      <td>-0.219315</td>\n",
       "      <td>-0.074097</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.422600</td>\n",
       "      <td>-0.040492</td>\n",
       "      <td>2.767105</td>\n",
       "      <td>-0.219315</td>\n",
       "      <td>-0.074097</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.339516</td>\n",
       "      <td>-1.612827</td>\n",
       "      <td>-0.232520</td>\n",
       "      <td>-0.219315</td>\n",
       "      <td>-0.909146</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  educational-num  capital-gain  capital-loss  hours-per-week  \\\n",
       "0 -1.025420        -1.219743     -0.232520     -0.219315       -0.074097   \n",
       "1 -0.034670        -0.433575     -0.232520     -0.219315        0.760953   \n",
       "2 -0.796786         0.745676     -0.232520     -0.219315       -0.074097   \n",
       "3  0.422600        -0.040492      2.767105     -0.219315       -0.074097   \n",
       "4 -0.339516        -1.612827     -0.232520     -0.219315       -0.909146   \n",
       "\n",
       "   workclass_Local-gov  workclass_Private  workclass_Self-emp-inc  \\\n",
       "0                  0.0                1.0                     0.0   \n",
       "1                  0.0                1.0                     0.0   \n",
       "2                  1.0                0.0                     0.0   \n",
       "3                  0.0                1.0                     0.0   \n",
       "4                  0.0                1.0                     0.0   \n",
       "\n",
       "   workclass_Self-emp-not-inc  workclass_State-gov  ...  \\\n",
       "0                         0.0                  0.0  ...   \n",
       "1                         0.0                  0.0  ...   \n",
       "2                         0.0                  0.0  ...   \n",
       "3                         0.0                  0.0  ...   \n",
       "4                         0.0                  0.0  ...   \n",
       "\n",
       "   native-country_Puerto-Rico  native-country_Scotland  native-country_South  \\\n",
       "0                         0.0                      0.0                   0.0   \n",
       "1                         0.0                      0.0                   0.0   \n",
       "2                         0.0                      0.0                   0.0   \n",
       "3                         0.0                      0.0                   0.0   \n",
       "4                         0.0                      0.0                   0.0   \n",
       "\n",
       "   native-country_Taiwan  native-country_Thailand  \\\n",
       "0                    0.0                      0.0   \n",
       "1                    0.0                      0.0   \n",
       "2                    0.0                      0.0   \n",
       "3                    0.0                      0.0   \n",
       "4                    0.0                      0.0   \n",
       "\n",
       "   native-country_Trinadad&Tobago  native-country_United-States  \\\n",
       "0                             0.0                           1.0   \n",
       "1                             0.0                           1.0   \n",
       "2                             0.0                           1.0   \n",
       "3                             0.0                           1.0   \n",
       "4                             0.0                           1.0   \n",
       "\n",
       "   native-country_Vietnam  native-country_Yugoslavia  income_>50K  \n",
       "0                     0.0                        0.0          0.0  \n",
       "1                     0.0                        0.0          0.0  \n",
       "2                     0.0                        0.0          1.0  \n",
       "3                     0.0                        0.0          1.0  \n",
       "4                     0.0                        0.0          0.0  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df_num, df_cat], axis=1)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.pop('income_>50K')\n",
    "\n",
    "X = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into train and test\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(31462, 80) (31462,)\n",
      "(13485, 80) (13485,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, y_train.shape)\n",
    "print(X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Training the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "clf_logit = LogisticRegression()\n",
    "\n",
    "clf_logit.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>28131</th>\n",
       "      <td>-1.254055</td>\n",
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       "      <td>-0.23252</td>\n",
       "      <td>4.122404</td>\n",
       "      <td>-1.326671</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>-0.23252</td>\n",
       "      <td>-0.219315</td>\n",
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       "      <td>0.0</td>\n",
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       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            age  educational-num  capital-gain  capital-loss  hours-per-week  \\\n",
       "22251 -1.635113        -1.612827      -0.23252     -0.219315       -2.579245   \n",
       "15880  0.193965         0.352592      -0.23252     -0.219315       -0.074097   \n",
       "21910  0.270177         0.352592      -0.23252     -0.219315        0.760953   \n",
       "28131 -1.254055        -1.612827      -0.23252      4.122404       -1.326671   \n",
       "27660  0.193965        -0.040492      -0.23252     -0.219315        0.760953   \n",
       "\n",
       "       workclass_Local-gov  workclass_Private  workclass_Self-emp-inc  \\\n",
       "22251                  0.0                1.0                     0.0   \n",
       "15880                  0.0                1.0                     0.0   \n",
       "21910                  0.0                0.0                     0.0   \n",
       "28131                  0.0                1.0                     0.0   \n",
       "27660                  0.0                0.0                     0.0   \n",
       "\n",
       "       workclass_Self-emp-not-inc  workclass_State-gov  ...  \\\n",
       "22251                         0.0                  0.0  ...   \n",
       "15880                         0.0                  0.0  ...   \n",
       "21910                         1.0                  0.0  ...   \n",
       "28131                         0.0                  0.0  ...   \n",
       "27660                         1.0                  0.0  ...   \n",
       "\n",
       "       native-country_Portugal  native-country_Puerto-Rico  \\\n",
       "22251                      0.0                         0.0   \n",
       "15880                      0.0                         0.0   \n",
       "21910                      0.0                         0.0   \n",
       "28131                      0.0                         0.0   \n",
       "27660                      0.0                         0.0   \n",
       "\n",
       "       native-country_Scotland  native-country_South  native-country_Taiwan  \\\n",
       "22251                      0.0                   0.0                    0.0   \n",
       "15880                      0.0                   0.0                    0.0   \n",
       "21910                      0.0                   0.0                    0.0   \n",
       "28131                      0.0                   0.0                    0.0   \n",
       "27660                      0.0                   0.0                    0.0   \n",
       "\n",
       "       native-country_Thailand  native-country_Trinadad&Tobago  \\\n",
       "22251                      0.0                             0.0   \n",
       "15880                      0.0                             0.0   \n",
       "21910                      0.0                             0.0   \n",
       "28131                      0.0                             0.0   \n",
       "27660                      0.0                             0.0   \n",
       "\n",
       "       native-country_United-States  native-country_Vietnam  \\\n",
       "22251                           1.0                     0.0   \n",
       "15880                           1.0                     0.0   \n",
       "21910                           1.0                     0.0   \n",
       "28131                           1.0                     0.0   \n",
       "27660                           1.0                     0.0   \n",
       "\n",
       "       native-country_Yugoslavia  \n",
       "22251                        0.0  \n",
       "15880                        0.0  \n",
       "21910                        0.0  \n",
       "28131                        0.0  \n",
       "27660                        0.0  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = clf_logit.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6: Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8459770114942529"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "metrics.accuracy_score(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2467b192610>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics.plot_confusion_matrix(clf_logit, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.88      0.93      0.90     10192\n",
      "         1.0       0.72      0.60      0.65      3293\n",
      "\n",
      "    accuracy                           0.85     13485\n",
      "   macro avg       0.80      0.76      0.78     13485\n",
      "weighted avg       0.84      0.85      0.84     13485\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, y_test_pred))\n",
    "\n",
    "# Number of features = 80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.902853252772996\n"
     ]
    }
   ],
   "source": [
    "prob = clf_logit.predict_proba(X_test)\n",
    "prob = prob[:, 1]\n",
    "roc_auc = metrics.roc_auc_score(y_test, prob)\n",
    "print(roc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ploting roc curve\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test, prob)\n",
    "\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "\n",
    "# Plot the roc curve for the model\n",
    "plt.plot(fpr, tpr, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x24601791ac0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6GsFLQGdgGfCMiOwAzgWmqOp79RBbozPp9W8B+Netg4m2vgFjTCNRXSJIAfqrakBE4oCDQE9V3Vc/oTUuqsrGfdkAnN+rtcfRGGNM6Kq7bC1Q1QCAquYDm080CYjIGBHZJCJpIjKlijIjRCRVRNaJyKIT2X9D8r8fOFM0TBrZ0+NIjDHmxFRXIzhdRFYH3wvQI7gsgKpq/+p2HOxjeA74Ic48BstFZI6qri9VpgXwPDBGVXeKSNuTPxTvHMkt4I3lziRud15oicAY07hUlwh+UMt9nwOkqepWABF5A7gcWF+qzHXAu6q6E0BVD9TyOz3xxIcbOHbcz/SfDqJJtA3MaoxpXKobdK62A811BErPdZwODC5XpjcQIyKf4Yxw+ldVfbX8jkTkduB2gC5dutQyrLqVX1hUcqfQD/u08zgaY4w5cW7e2lLZjOxabjkaGARcClwM/FZEKgzMo6ozVDVFVVPatGlT95HWwr9XpuMPKL+/vC++KJuE3hjT+ITyZPHJSse5/bRYJ5zhKcqXOaiqOUCOiCwGBgCbXYyrTr25fCcAl/Xv4HEkxhhzckKqEYhIvIicdoL7Xg70EpHuIhILXAvMKVfmfWCYiESLSFOcpqMNJ/g9nlm0OYO1u49ycd92JNtcA8aYRqrGRCAiPwJSgXnB5YEiUv6EXoGq+oFJwMc4J/e3VHWdiEwUkYnBMhuC+12N8+Dai6q69iSPpd79v/mbAJg8+kRzpDHGNByhNA09hnMH0GcAqpoqIt1C2bmqfgh8WO6z6eWW/wz8OZT9NSRPf7KZ1elZtEqIpVe7xJo3MMaYBiqUpiG/qma5Hkkjoqr89dMtALw/ycbfM8Y0bqHUCNaKyHWAT0R6AXcBS9wNq2F7dalzZ+15PVvRKbmpx9EYY0zthFIjuBNnvuLjwOs4w1Hf42JMDVpRQHl0zjoA/nTVAI+jMcaY2gulRnCaqv4G+I3bwTQGa3Y7rWSX9mtPxxY234AxpvELpUbwFxHZKCK/F5G+rkfUwP1hrjNCxoOXnO5xJMYYUzdqTASqOhIYAWQAM0RkjYg87HZgDdFxfxErdhwmsUm09Q0YY8JGSA+Uqeo+VX0GmIjzTMEjbgbVUH20xhmF+9Zh3T2OxBhj6k4oD5T9QEQeE5G1wLM4dwx1cj2yBqYooPz6bWdU7l9c0MPjaIwxpu6E0ln8D2AWMFpVy48VFDE+Wb+fgqIAPx3ShfhYG2raGBM+akwEqjqkPgJp6FbuOATAr8dYJ7ExJrxUmQhE5C1VvUZE1lB2+OiQZigLN2+tSKdDUhzN42K8DsUYY+pUdTWCu4M/L6uPQBqyD1bvJSuvkKE9WnkdijHG1LkqO4tVdW/w7a9UdUfpF/Cr+gmvYbj3rVQA7hzVy9tAjDHGBaHcPvrDSj4bW9eBNFQ7MnMo8AdIio+hT4fmXodjjDF1rro+gl/iXPmfKiKrS61KBL50O7CG4g8fOPPk/PPWczyOxBhj3FFdH8HrwEfAk8CUUp9nq+ohV6NqQD5Zv58m0VH079TC61CMMcYV1SUCVdXtInJH+RUi0jISkkHagWMAjDnjFI8jMcYY99RUI7gMWIlz+6iUWqfAqS7G1SD833+dyWeuH9zV40iMMcY9VSYCVb0s+DMiB9bZfjCH91P3cPWgTpzTvaXX4RhjjGtCGWvoPBFJCL7/qYj8RUS6uB+at5Z8lwnA9UOsNmCMCW+h3D76ApArIgOAXwM7gH+6GlUD8I8vtzmdxB2TvA7FGGNcFerk9QpcDvxVVf+Kcwtp2AoElC0HjtG9dQJRUVLzBsYY04iFMvpotog8CNwADBMRHxDWA+689MU2AH40oIPHkRhjjPtCqRGMx5m4/mequg/oCPzZ1ag89uV3BwH45XCbd8AYE/5CmapyH/AakCQilwH5qvqq65F5ZPeRPD7blMENQ7pas5AxJiKEctfQNcAy4GrgGuBrEbnK7cC88t8N+wEYeXobjyMxxpj6EUofwW+As1X1AICItAEWAG+7GZhX/vHldpKbxjDytLZeh2KMMfUilD6CqOIkEJQZ4naNjqqy9WAOACLWLGSMiQyh1AjmicjHOPMWg9N5/KF7IXnnyzTnIbJrzu7scSTGGFN/Qpmz+H4R+QlwPs54QzNUdbbrkXng3W/SAbjyrE4eR2KMMfWnuvkIegFTgR7AGmCyqu6ur8C8cLwoAEDvdmH9vJwxxpRRXVv/TGAucCXOCKT/d6I7F5ExIrJJRNJEZEo15c4WkSKv70Y6dKyAji3ivQzBGGPqXXVNQ4mq+vfg+00i8s2J7Dj4BPJzOFNdpgPLRWSOqq6vpNwfgY9PZP91TVX5ZudhLutvTxMbYyJLdYkgTkTO5Pt5COJLL6tqTYnhHCBNVbcCiMgbOOMVrS9X7k7gHeDsE4y9Tr3zzW6O+wOc2aWFl2EYY0y9qy4R7AX+Ump5X6llBUbVsO+OwK5Sy+nA4NIFRKQjMC64ryoTgYjcDtwO0KWLOyNgv7JkOwAX/sCeHzDGRJbqJqYZWct9V3YjvpZbngY8oKpF1d23r6ozgBkAKSkp5fdRJ3IK/MTH+GifZH0ExpjIEspzBCcrHSh9Q34nYE+5MinAG8Ek0Bq4RET8qvqei3FVsHLHIbZm5PDAmNPr82uNMaZBcDMRLAd6iUh3YDdwLXBd6QKlp8EUkZeBufWdBAC+2OI8SDaoa3J9f7UxxnjOtUSgqn4RmYRzN5APmKmq60RkYnD9dLe++0St3HkYwDqKjTERqcZEIE67zfXAqar6u+B8xaeo6rKatlXVDyk3HEVVCUBVbw4pYhes251Fy4RYYnxhOYSSMcZUK5Qz3/PAucCE4HI2zvMBYaGwKEBmTgGn2dPExpgIFUrT0GBVPUtEvgVQ1cMiEutyXPVm5Q6nWWjU6XbbqDEmMoVSIygMPv2rUDIfQcDVqOrRpn3ZAAw5tZXHkRhjjDdCSQTPALOBtiLyv8AXwBOuRlWPNgYTQfc2CR5HYowx3ghlGOrXRGQlcCHOQ2JXqOoG1yOrJ/uP5gOQEOvzOBJjjPFGKHcNdQFygf+U/kxVd7oZWH3ZkZnD0B6tbEYyY0zECqWz+AOc/gEB4oDuwCagr4tx1QtV5buMHFK6tvQ6FGOM8UwoTUP9Si+LyFnAL1yLqB7tCzYLtUls4nEkxhjjnRN+gio4/LSnQ0bXlXW7jwLQuaUNNGeMiVyh9BHcV2oxCjgLyHAtonqUmXMcgHNPbe1xJMYY451Q+ghKP3Lrx+kzeMedcOrXviwnESTFx3gciTHGeKfaRBB8kKyZqt5fT/HUqy/SMkiKjyExzs1BWI0xpmGrso9ARKJVtQinKSgsLd9+mEFdk4mKsltHjTGRq7pL4WU4SSBVROYA/wZyileq6rsux+YqVWeiM6sNGGMiXShnwZZAJs68wsXPEyjQqBPB/qNO/0Dn5KYeR2KMMd6qLhG0Dd4xtJbvE0AxV+YNrk9rdmcBcFbXFt4GYowxHqsuEfiAZoQ2CX2j8+byXQD0aNPM40iMMcZb1SWCvar6u3qLpJ41DQ4y17WVjTpqjIls1T1ZHNa30ny8bh992jf3OgxjjPFcdYngwnqLwgMBVbKPF3odhjHGeK7KRKCqh+ozkPq050gehUXKyNNsekpjjDnhQefCwaLNzlBJZ3RM8jgSY4zxXkQmgi+2HATg0n7tPY7EGGO8F5GJ4Gi+0zeQ0MSeKjbGmIhMBHkFRbRrbpPRGGMMRGgiWLsni0Fdk70OwxhjGoSISwSBgJJfGKBJtM/rUIwxpkGIuESw9eAxAHq2taEljDEGIjAR7DyUC0Dvdok1lDTGmMgQeYkg00kEfTrY8BLGGAMuJwIRGSMim0QkTUSmVLL+ehFZHXwtEZEBbsYDsH7vUQBOaR7n9lcZY0yj4FoiCM53/BwwFugDTBCRPuWKbQOGq2p/4PfADLfiKXY413mGwGfTUxpjDOBujeAcIE1Vt6pqAfAGcHnpAqq6RFUPBxe/Ajq5GA8AW/ZnEx9jdwwZY0wxNxNBR2BXqeX04GdVuRX4qLIVInK7iKwQkRUZGRm1CupQTgGdkuNrtQ9jjAknbiaCkGc2E5GROInggcrWq+oMVU1R1ZQ2bdrUKqjj/gCnJFn/gDHGFHNzsJ10oHOp5U7AnvKFRKQ/8CIwVlUzXYwHVeW4P8CATi3c/BpjjGlU3KwRLAd6iUh3EYkFrgXmlC4gIl2Ad4EbVHWzi7EAkH44D4AibfRTLhtjTJ1xrUagqn4RmQR8DPiAmaq6TkQmBtdPBx4BWgHPiwiAX1VT3IopM6cAgG6tmrr1FcYY0+i4Og6zqn4IfFjus+ml3t8G3OZmDKUdyXUSQadkSwTGGFMsop4szi8sAiBK7BkCY4wpFlGJoKDI6RtokxjrcSTGGNNwRFQiOHTsOADN42I8jsQYYxqOiEoExcNLtG5ms5MZY0yxiEoE2w7mEB0lRNk4Q8YYUyKiEkF8jA9/wJ4hMMaY0iIqEWzan23PEBhjTDmuPkfQ0BzKKaDIagTGGFNGRCWC2OgoYn0RVQkyxpgaRdRZ0V8UsEnrjTGmnIhKBIVFSrTP7hgyxpjSIioR+AMBYqIi6pCNMaZGEXVWPJrnp0lMRB2yMcbUKGLOioVFAfIKi2iZYOMMGWNMaRGTCLLz/V6HYIwxDVLEJILjfmcIahtwzhhjyoqYRFD8IFmzJhH16IQxxtQoYhJBIOD8tAHnjDGmrIhJBMUT1tuDxcYYU1bEnBaLm4ZsmkpjjCkrYhJBoKRGYInAGGNKi5ie0wK/00ngsxpBWCksLCQ9PZ38/HyvQzGmQYiLi6NTp07ExIR+h2TEJIJjx53nCOJjfR5HYupSeno6iYmJdOvWDbEkbyKcqpKZmUl6ejrdu3cPebuIaxqKi7FEEE7y8/Np1aqVJQFjABGhVatWJ1xDjphEEMwD1lkchiwJGPO9k/n/EDGJoLhGYH3FxhhTVgQlAuenXT2autasWe0nO1qxYgV33XVXleu3b9/O66+/HnJ5gG7dutGvXz/69+/P8OHD2bFjR63jrCvTp0/n1VdfrZN97d27l8suu6zMZ3fffTcdO3YkUPwkKfDYY48xderUMuW6devGwYMHAdi3bx/XXnstPXr0oE+fPlxyySVs3ry5VrEdP36c8ePH07NnTwYPHsz27dsrLffmm2/Sv39/+vbty69//esat8/IyGDMmDG1iq20CEoEViMwDVdKSgrPPPNMlevLJ4KayhdbuHAhq1evZsSIEfzhD3+odZyqWubkerImTpzIjTfeWOv9APzlL3/h5z//eclyIBBg9uzZdO7cmcWLF4e0D1Vl3LhxjBgxgu+++47169fzxBNPsH///lrF9tJLL5GcnExaWhr33nsvDzzwQIUymZmZ3H///Xz66aesW7eO/fv38+mnn1a7fZs2bWjfvj1ffvllreIrFjF3DanaA2Xh7vH/rGP9nqN1us8+HZrz6I/6nvB2qampTJw4kdzcXHr06MHMmTNJTk5m+fLl3HrrrSQkJHD++efz0UcfsXbtWj777DOmTp3K3LlzWbRoEXfffTfg1GAXL17MlClT2LBhAwMHDuSmm27izDPPLCl/7Ngx7rzzTlasWIGI8Oijj3LllVeWiefcc88tSRwZGRlMnDiRnTt3AjBt2jTOO+88MjIyuO6668jMzOTss89m3rx5rFy5kmPHjjF27FhGjhzJ0qVLee+993jrrbd46623OH78OOPGjePxxx8nJyeHa665hvT0dIqKivjtb3/L+PHjmTJlCnPmzCE6OprRo0czdepUHnvsMZo1a8bkyZOr/F2NGDGCwYMHs3DhQo4cOcJLL73EsGHDKvyu33nnnTJJbuHChZxxxhmMHz+eWbNmMWLEiBr/XgsXLiQmJoaJEyeWfDZw4MAT/bNX8P777/PYY48BcNVVVzFp0iRUtUzLxNatW+nduzdt2rQB4KKLLuKdd97hwgsvrHb7K664gtdee43zzjuv1nFGTo2geKwhSwSmHtx444388Y9/ZPXq1fTr14/HH38cgFtuuYXp06ezdOlSfL7K72CbOnUqzz33HKmpqXz++efEx8fz1FNPMWzYMFJTU7n33nvLlP/9739PUlISa9asYfXq1YwaNarCPufNm8cVV1wBOM0m9957L8uXL+edd97htttuA+Dxxx9n1KhRfPPNN4wbN64kUQBs2rSJG2+8kW+//ZZNmzaxZcsWli1bRmpqKitXrmTx4sXMmzePDh06sGrVKtauXcuYMWM4dOgQs2fPZt26daxevZqHH3445N8VgN/vZ9myZUybNq3M58W2bdtGcnIyTZo0Kfls1qxZTJgwgXHjxjF37lwKCwur+jOVWLt2LYMGDaqxHMCwYcMYOHBghdeCBQsqlN29ezedO3cGIDo6mqSkJDIzM8uU6dmzJxs3bmT79u34/X7ee+89du3aVeP2KSkpfP755yHFXJOIqREUNw1ZHghfJ3Pl7oasrCyOHDnC8OHDAbjpppu4+uqrOXLkCNnZ2QwdOhSA6667jrlz51bY/rzzzuO+++7j+uuv5yc/+QmdOnWq9vsWLFjAG2+8UbKcnJxc8n7kyJHs37+ftm3bllw1L1iwgPXr15eUOXr0KNnZ2XzxxRfMnj0bgDFjxpTZT9euXRkyZAgA8+fPZ/78+Zx55pkAHDt2jC1btjBs2DAmT57MAw88wGWXXcawYcPw+/3ExcVx2223cemll1Zoy6/qd1XsJz/5CQCDBg2qtH197969JVfSAAUFBXz44Yc8/fTTJCYmMnjwYObPn8+ll15aZf/gifYbnsjJt7glorrvS05O5oUXXmD8+PFERUUxdOhQtm7dWuP2bdu2Zc+ePScSepVcrRGIyBgR2SQiaSIypZL1IiLPBNevFpGz3Iql+NdpicB4pbL/1JWZMmUKL774Inl5eQwZMoSNGzfWuN+qTmYLFy5kx44d9O3bl0ceeQRw2tCXLl1Kamoqqamp7N69m8TExGrjS0hIKPN9Dz74YMn2aWlp3HrrrfTu3ZuVK1fSr18/HnzwQX73u98RHR3NsmXLuPLKK3nvvfdOuIOz+Erf5/Ph91ecXCo+Pr7MPfPz5s0jKyuLfv360a1bN7744gtmzZoFQKtWrTh8+HCZ7bOzs2nRogV9+/Zl5cqVIcV0IjWCTp06lVzd+/1+srKyaNmyZYVyP/rRj/j6669ZunQpp512Gr169apx+/z8fOLj40OKuSauJQIR8QHPAWOBPsAEEelTrthYoFfwdTvwglvxWB+BqS9JSUkkJyeXXDn+85//ZPjw4SQnJ5OYmMhXX30FUOYqvrTvvvuOfv368cADD5CSksLGjRtJTEwkOzu70vKjR4/m2WefLVkuf7KLj49n2rRpvPrqqxw6dKhC+dTUVADOP/983nrrLcC56i+/n2IXX3wxM2fO5NixY4DTfHHgwAH27NlD06ZN+elPf8rkyZP55ptvOHbsGFlZWVxyySVMmzat5Ltq+l2Fqnfv3mVqCrNmzeLFF19k+/btbN++nW3btjF//nxyc3O54IILmDNnTsnv8d1332XAgAH4fD5GjRrF8ePH+fvf/16yr+XLl7No0aIK3/n555+XJMHSr4suuqhC2R//+Me88sorALz99tuMGjWq0qR94MABwPnbPf/88yXNddVtv3nzZs4444yQf1fVcbNp6BwgTVW3AojIG8DlwPpSZS4HXlXnLP2ViLQQkfaqureugwnYA2XGJbm5uWWab+677z5eeeWVkg7QU089lX/84x+AcxfIz3/+cxISEhgxYgRJSUkV9jdt2jQWLlyIz+ejT58+jB07lqioKKKjoxkwYAA333xzSbMMwMMPP8wdd9zBGWecgc/n49FHHy1pUinWvn17JkyYwHPPPcczzzzDHXfcQf/+/fH7/VxwwQVMnz6dRx99lAkTJvDmm28yfPhw2rdvT2JiYskJv9jo0aPZsGED5557LuDcPvuvf/2LtLQ07r//fqKiooiJieGFF14gOzubyy+/nPz8fFSVp59+usLxVvW7CkVCQgI9evQgLS2NDh068PHHH/O3v/2tzPrzzz+f//znP4wfP55JkyZx/vnnIyK0bduWF198EXCaW2bPns0999zDU089RVxcHN26dWPatGkhx1KZW2+9lRtuuIGePXvSsmXLMsl/4MCBJYnx7rvvZtWqVQA88sgj9O7du8btFy5cyKWXXlqr+Eqoqisv4CrgxVLLNwDPliszFzi/1PKnQEol+7odWAGs6NKli56MFdsP6a/+tVJ3H849qe1Nw7R+/XqvQzgh2dnZJe+ffPJJveuuuzyMpqz8/HwtLCxUVdUlS5bogAEDvA0oRO+++67+5je/8TqMejds2DA9dOhQpesq+38BrNAqztdu1ggqu/Qu3wgZShlUdQYwAyAlJSW0htZyBnVNZlDX5JoLGuOiDz74gCeffBK/30/Xrl15+eWXvQ6pxM6dO7nmmmsIBALExsaWaSZpyMaNG1fhTpxwl5GRwX333VemQ7823EwE6UDnUsudgPJd3KGUMSZsjB8/nvHjx3sdRqV69erFt99+63UYJ6W4TT1StGnTpuR24Lrg5l1Dy4FeItJdRGKBa4E55crMAW4M3j00BMhSF/oHTHjTEO/GMSYSnMz/B9dqBKrqF5FJwMeAD5ipqutEZGJw/XTgQ+ASIA3IBW5xKx4TnuLi4sjMzLShqI3h+/kI4uLiTmg7aWxXUykpKbpixQqvwzANhM1QZkxZVc1QJiIrVTWlsm0i5sliE55iYmJOaCYmY0xFETPWkDHGmMpZIjDGmAhnicAYYyJco+ssFpEM4GSnWmoNHKzDcBoDO+bIYMccGWpzzF1VtU1lKxpdIqgNEVlRVa95uLJjjgx2zJHBrWO2piFjjIlwlgiMMSbCRVoimOF1AB6wY44MdsyRwZVjjqg+AmOMMRVFWo3AGGNMOZYIjDEmwoVlIhCRMSKySUTSRGRKJetFRJ4Jrl8tImd5EWddCuGYrw8e62oRWSIiA7yIsy7VdMylyp0tIkUiclV9xueGUI5ZREaISKqIrBORipPuNjIh/NtOEpH/iMiq4DE36lGMRWSmiBwQkbVVrK/781dVU5c11hfOkNffAacCscAqoE+5MpcAH+HMkDYE+NrruOvhmIcCycH3YyPhmEuV+y/OkOdXeR13PfydW+DMC94luNzW67jr4ZgfAv4YfN8GOATEeh17LY75AuAsYG0V6+v8/BWONYJzgDRV3aqqBcAbwOXlylwOvKqOr4AWItK+vgOtQzUes6ouUdXDwcWvcGaDa8xC+TsD3Am8Axyoz+BcEsoxXwe8q6o7AVS1sR93KMesQKI4E1I0w0kE/voNs+6o6mKcY6hKnZ+/wjERdAR2lVpOD352omUakxM9nltxrigasxqPWUQ6AuOA6fUYl5tC+Tv3BpJF5DMRWSkiN9ZbdO4I5ZifBX6AM83tGuBuVQ3UT3ieqPPzVzjOR1DZNFXl75ENpUxjEvLxiMhInERwvqsRuS+UY54GPKCqRWEye1koxxwNDAIuBOKBpSLylapudjs4l4RyzBcDqcAooAfwiYh8rqpHXY7NK3V+/grHRJAOdC613AnnSuFEyzQmIR2PiPQHXgTGqmpmPcXmllCOOQV4I5gEWgOXiIhfVd+rlwjrXqj/tg+qag6QIyKLgQFAY00EoRzzLcBT6jSgp4nINuB0YFn9hFjv6vz8FY5NQ8uBXiLSXURigWuBOeXKzAFuDPa+DwGyVHVvfQdah2o8ZhHpArwL3NCIrw5Lq/GYVbW7qnZT1W7A28CvGnESgND+bb8PDBORaBFpCgwGNtRznHUplGPeiVMDQkTaAacBW+s1yvpV5+evsKsRqKpfRCYBH+PccTBTVdeJyMTg+uk4d5BcAqQBuThXFI1WiMf8CNAKeD54hezXRjxyY4jHHFZCOWZV3SAi84DVQAB4UVUrvQ2xMQjx7/x74GURWYPTbPKAqjba4alFZBYwAmgtIunAo0AMuHf+siEmjDEmwoVj05AxxpgTYInAGGMinCUCY4yJcJYIjDEmwlkiMMaYCGeJwDRIwdFCU0u9ulVT9lgdfN/LIrIt+F3fiMi5J7GPF0WkT/D9Q+XWLaltjMH9FP9e1gZH3GxRQ/mBInJJXXy3CV92+6hpkETkmKo2q+uy1ezjZWCuqr4tIqOBqaravxb7q3VMNe1XRF4BNqvq/1ZT/mYgRVUn1XUsJnxYjcA0CiLSTEQ+DV6trxGRCiONikh7EVlc6op5WPDz0SKyNLjtv0WkphP0YqBncNv7gvtaKyL3BD9LEJEPguPfrxWR8cHPPxORFBF5CogPxvFacN2x4M83S1+hB2siV4qIT0T+LCLLxRlj/hch/FqWEhxsTETOEWeeiW+DP08LPon7O2B8MJbxwdhnBr/n28p+jyYCeT32tr3sVdkLKMIZSCwVmI3zFHzz4LrWOE9VFtdojwV//g/wm+B7H5AYLLsYSAh+/gDwSCXf9zLB+QqAq4GvcQZvWwMk4AxvvA44E7gS+HupbZOCPz/DufouialUmeIYxwGvBN/H4owiGQ/cDjwc/LwJsALoXkmcx0od37+BMcHl5kB08P1FwDvB9zcDz5ba/gngp8H3LXDGIErw+u9tL29fYTfEhAkbeao6sHhBRGKAJ0TkApyhEzoC7YB9pbZZDswMln1PVVNFZDjQB/gyOLRGLM6VdGX+LCIPAxk4I7ReCMxWZwA3RORdYBgwD5gqIn/EaU76/ASO6yPgGRFpAowBFqtqXrA5qr98P4taEtAL2FZu+3gRSQW6ASuBT0qVf0VEeuGMRBlTxfePBn4sIpODy3FAFxr3eESmliwRmMbiepzZpwapaqGIbMc5iZVQ1cXBRHEp8E8R+TNwGPhEVSeE8B33q+rbxQsiclFlhVR1s4gMwhnv5UkRma+qvwvlIFQ1X0Q+wxk6eTwwq/jrgDtV9eMadpGnqgNFJAmYC9wBPIMz3s5CVR0X7Fj/rIrtBbhSVTeFEq+JDNZHYBqLJOBAMAmMBLqWLyAiXYNl/g68hDPd31fAeSJS3ObfVER6h/idi4Ergtsk4DTrfC4iHYBcVf0XMDX4PeUVBmsmlXkDZ6CwYTiDqRH8+cvibUSkd/A7K6WqWcBdwOTgNknA7uDqm0sVzcZpIiv2MXCnBKtHInJmVd9hIoclAtNYvAakiMgKnNrBxkrKjABSReRbnHb8v6pqBs6JcZaIrMZJDKeH8oWq+g1O38EynD6DF1X1W6AfsCzYRPMb4A+VbD4DWF3cWVzOfJx5aReoM/0iOPNErAe+EWfS8r9RQ409GMsqnKGZ/4RTO/kSp/+g2EKgT3FnMU7NISYY29rgsolwdvuoMcZEOKsRGGNMhLNEYIwxEc4SgTHGRDhLBMYYE+EsERhjTISzRGCMMRHOEoExxkS4/w9rDx3GBXFGtQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics.plot_roc_curve(clf_logit, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Elimination using RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 9))\n",
    "sns.heatmap(X_train.corr(), cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "clf_logit_rfe = LogisticRegression()\n",
    "\n",
    "rfe = RFE(clf_logit_rfe)\n",
    "rfe = rfe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Columns</th>\n",
       "      <th>Included</th>\n",
       "      <th>Ranking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>educational-num</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>capital-gain</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hours-per-week</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>workclass_Self-emp-not-inc</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>workclass_Without-pay</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>marital-status_Married-AF-spouse</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>marital-status_Married-civ-spouse</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>marital-status_Never-married</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>occupation_Exec-managerial</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>occupation_Farming-fishing</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>occupation_Handlers-cleaners</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>occupation_Machine-op-inspct</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>occupation_Other-service</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>occupation_Priv-house-serv</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>occupation_Prof-specialty</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>occupation_Tech-support</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>relationship_Other-relative</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>relationship_Own-child</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>relationship_Wife</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>race_Asian-Pac-Islander</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>race_Black</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>race_Other</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>race_White</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>gender_Male</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>native-country_China</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>native-country_Columbia</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>native-country_Dominican-Republic</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>native-country_Haiti</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>native-country_Ireland</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>native-country_Italy</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>native-country_Laos</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>native-country_Mexico</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>native-country_Nicaragua</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>native-country_Peru</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>native-country_Portugal</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>native-country_Scotland</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>native-country_South</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>native-country_Thailand</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>native-country_Vietnam</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Columns  Included  Ranking\n",
       "0                                 age      True        1\n",
       "1                     educational-num      True        1\n",
       "2                        capital-gain      True        1\n",
       "4                      hours-per-week      True        1\n",
       "8          workclass_Self-emp-not-inc      True        1\n",
       "10              workclass_Without-pay      True        1\n",
       "11   marital-status_Married-AF-spouse      True        1\n",
       "12  marital-status_Married-civ-spouse      True        1\n",
       "14       marital-status_Never-married      True        1\n",
       "19         occupation_Exec-managerial      True        1\n",
       "20         occupation_Farming-fishing      True        1\n",
       "21       occupation_Handlers-cleaners      True        1\n",
       "22       occupation_Machine-op-inspct      True        1\n",
       "23           occupation_Other-service      True        1\n",
       "24         occupation_Priv-house-serv      True        1\n",
       "25          occupation_Prof-specialty      True        1\n",
       "28            occupation_Tech-support      True        1\n",
       "31        relationship_Other-relative      True        1\n",
       "32             relationship_Own-child      True        1\n",
       "34                  relationship_Wife      True        1\n",
       "35            race_Asian-Pac-Islander      True        1\n",
       "36                         race_Black      True        1\n",
       "37                         race_Other      True        1\n",
       "38                         race_White      True        1\n",
       "39                        gender_Male      True        1\n",
       "41               native-country_China      True        1\n",
       "42            native-country_Columbia      True        1\n",
       "44  native-country_Dominican-Republic      True        1\n",
       "52               native-country_Haiti      True        1\n",
       "59             native-country_Ireland      True        1\n",
       "60               native-country_Italy      True        1\n",
       "63                native-country_Laos      True        1\n",
       "64              native-country_Mexico      True        1\n",
       "65           native-country_Nicaragua      True        1\n",
       "67                native-country_Peru      True        1\n",
       "70            native-country_Portugal      True        1\n",
       "72            native-country_Scotland      True        1\n",
       "73               native-country_South      True        1\n",
       "75            native-country_Thailand      True        1\n",
       "78             native-country_Vietnam      True        1"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df = pd.DataFrame({'Columns' : X_train.columns, 'Included' : rfe.support_, 'Ranking' : rfe.ranking_})\n",
    "\n",
    "temp_df.loc[temp_df.Included == True, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_cols = list(X_train.columns[rfe.support_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31462, 40)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_rfe = X_train[X_train_cols]\n",
    "\n",
    "X_train_rfe.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JiHtLcR4C9kz3DwDmUSy7vA8wPZWPAb4cEXsBo4Bfp/JfAD+LiL3TsV9SbqCkU4GPA4dFxKI29IOZmZmZmZmtIkkdeqtXvuRn7fR0RMxK9x8EtgUGRsTkVHY5cF0b4twVES+l+zOASyX1BG4qxa+2zYsAaYTJ/kAjsBcwI73Y1wf+neo3ATdUBomIRkl/TcmYocBPgWFAd+AeSX2B/YDrSm+g5jVhDwZ2LpX3l9Qv3f8s8CxFMqXqmrSSTgJOAtjxy19niw8fWuNQzczMzMzMzKpzQmXttKR0vwkY2ELdRlaMROpd8dzC5jsRMUXSMOCjwJWSzgdeY8VIl883V62IERQrvF8eEd+qsv/FzfOmSLoD2BSYGRGfB+4BPgwsA/4EjKVIqIxKbX4ljXip1A3Yt3L0SUqwzKMYPfN24Okq2xIRYyhGv/C+/5uae7V7MzMzMzOzLsVzqNjabAHwcmnek88CzaNVnqEYPQJwZK0AkrYG/h0RvwP+B3hXRNwYEQ3pNjNVfb+kjdLcJIcBU4EJwJGSNkmxNkrxVhIRH0yxmpMzU4DTgfsi4j/AIGAn4JGIeBV4WtJRKaYk7ZG2uxM4tdT2htJuHga+AIyXtHmt4zUzMzMzMzNbHU6orDs+B5wvaQ7FCI3vpfILgFMkTQM2bmH74cAsSQ9TzEvyixr17gWupJhj5YaImBkRjwLfppjcdg5wF7BZG9o8nWLEypT0eA4wJyKaR40cA5wgaTbwCNB8bc5pwJA0Ae+jwMnloGmullHArZJaOmYzMzMzMzNbTV422dYKEfEMsGvp8QWlp99dpf5jwO6lom+n8rEUl9g017ucYu6V1vw7Ik6tLIyIa4Frq5T3rRUoXbKzXunxSRXPPw18qMp28ykmwa0sH126fwdwR619m5mZmZmZma0OJ1SsS1v4Rt4pVLbbuClrvMbleWe0Hva7L2WNN+XEX2WNN+L3J7deqR16ZJ4QfMMN82bH//Z63o/gt/drzBpv9tN5O3DH/lnDATCw1/Ks8f78Ss+s8fK2Dnp1y/uZlfv/PYub8r5mpv27V9Z4ryzNe8TdMn/G5D4f3ZT39ZL79Qyw0Ua5z0neVjZmnmkt9++l3H8n5H4N5n7N5O6/3O+R3OejI+Tuw+WZ3yNLM/dh7+6Zz7FnX6ypq86h4oSKtVnlqBYzMzMzMzOzrsoJFTMzMzMzMzNbZV11hErXPGozMzMzMzMzW2dI+pCkxyX9VdIZVZ4fIOlmSbMlPSLpuNXdpxMqJZKeaeuqMJJGSxrVwe05M53oOZJmSdqnlfpjJR2Z7h+Qtp2Vljiua5IGSvpiC8+fLOnYNdkmMzMzMzMza11nr/IjqTvwK+DDwM7ApyTtXFHtS8CjEbEHxSq3P5G0WhO2+ZKfJJ2AuiFpX+BjwLsiYklK9LTnZB8DXBARl3VIA/MbCHwR+HW1JyPi4jXaGjMzMzMzM1tbDAX+GhFPAUi6BjgUeLRUJ4B+kgT0BV4CVmtVh3VihIqkb0g6Ld3/maS70/33Sfq9pE9JmitpnqQfl7Z7XdL3JE0H9i2Vry/pdkknpsfHplEisyVdWWX/J0qakZ6/QdIGqfyotM/Zkqaksl0kPZBGjsyRtH2Nw9oMmB8RS6BYKjgink8x9pI0WdKDku6QtFlFez4PHA2cLemqKu39TKkNv21OJqX++HGK+ydJQyVNkvSUpENSnZGS/pj653FJ36lxTkZLurS0/Wml576W+mWepNNT8bnAtqlN59eINyrdn5Ta+YCkv0g6IJV3l3RBOtdzJH25Rt+amZmZmZlZLurWoTdJJ0maWbqdVNGCLYB/lB4/m8rKLgLeCTwPzAW+EhGrtSDZOpFQAaYAB6T7Q4C+knoC+wNPAD8GDgIagL0lHZbq9gHmRcQ+EXFvKusL3Az8ISJ+J2kX4EzgoDQ06CtV9j8uIvZOz/8ZOCGVnw18MJUfkspOBn4REQ2prc/WOKY7gS1TwuDXkg4ESMf1S+DIiNgLuBT4QXnDiLgEGA98PSKOKT8n6Z3ACOA9qQ1NFKNZmvtjUor7GvB94P3A4cD3SmGGpm0agKMkDalxDDsBH0z1vyOpp6S9gOOAfYB3AydK2hM4A3gyIhoi4us14pX1iIihwOlAc1LnJOAdwJ4RsTvwlmRS6oM334z/vmt8G3ZlZmZmZmZmnSUixkTEkNJtTEWVamtuVy50/UFgFrA5xXfZiyT1X512rSsJlQeBvST1A5YA91EkKw4AXqFIEvwnIhopvmQPS9s1ATdUxPojcFlEXJEeHwRcHxHzASLipSr731XSPZLmUiQadknlU4GxaaRL8yVF9wH/LembwNYRsajaAUXE68BeFEmC/wDXShoJ7AjsCtwlaRbwbeDtLXfPSt6X4s5I278P2CY9txS4Pd2fC0yOiGXp/uBSjLsi4sXU9nEUiatqbo2IJanv/g1smureGBEL0zGOY0UyrD3GpZ8Pltp2MHBxOs+1ztVKb8ZN3n9ItSpmZmZmZmbWRlK3Dr21wbPAlqXHb6cYiVJ2HMVgiIiIvwJPUwwCWGXrREIlfel/hqKDpgH3AO8FtgX+3sKmiyOiqaJsKvDhdF0VFJmuysxWpbHAqRGxG/BdoHdq18kUCY8tgVmSBkXEHyhGqywC7pB0UAvH1RQRkyLiO8CpwBGpPY+kkRwNEbFbRHygVgxJW6bLaGZJOjltf3lp+x0jYnSqviwimo91OUVyijQMqjzfTmV/hKQvlfazeSpfUqrTlGJUyxxWa/cPmuPVqNIcuzkutO1cmZmZmZmZ2bplBrC9pHeomGj2kxRXbZT9nWJAAZI2pRis8NTq7HSdSKgkU4BR6ec9FJfWzALuBw6UtHGaK+RTwOQW4pwNvMiKyVEnAEdLGgQgaaMq2/QD/pkux3nzEhtJ20bE9Ig4G5hPcQnPNsBTEXEhxQnevVojJO1YMb9KA/A34HHgbSomrSVdRrNLlRAARMQ/SsmTi9PxHClpk+bjkbR1C/1RzfvTdusDhwFTI+JXpf1UZgLLpgCHSdpAUh+Ky4nuobjEqF+p3Wc2x2tHu+4ETpbUA2qeKzMzMzMzM8tIUofeWpOuUjgVuINiGo7/jYhHVKwWe3Kqdg6wX7qyZALwzeYrUVbVurTKzz0Uc53cFxELJS0G7omIf0r6FjCRYgTDbRHxx1ZinQ5cKum8iPiGpB8AkyU1AQ8DIyvqnwVMp0h4zGVFYuD8lBQRxQmbTTFXyGckLQP+xcpzk5T1BX4paSDFzMN/BU6KiKUqlka+UNIAinP4c+CRVo4JgIh4VNK3gTtVjJ1aRrF81N/asn1yL3AlsB3FXDMz27phRDwkaSzwQCq6JCIeBpA0VdI84P/aOI9KpUuAHYA5qX9/RzHxkJmZmZmZma3DIuI24LaKsotL958Hal7dsSq04goPs9aleVyGRMSpnd2WHD49aXLWN0CvzGO+lmd+e/bqnjdg4/I2XcHVZtd+Ju/q2EddeXLrlTrRak0pXkWPvKeDl5fmH8Q4oGfuo+5aumU+x7k/Y3JrrPP25X7PrQ1yv4PXpaHSZmbtdcWBB64zv0m2H/LLDv2t/cTML9dlX61LI1TMzKyOOZlitnbzO9jMzGpp48Sx6xwnVDpZmptlQpWn3hcRL67p9rQmIsZSTMJrZmZmZmZm1mU5odLJUtKkobPbYWZmZmZmZrZK2jBx7Lqoa47LWYMkPSNp4zbWHS1pVAe25WeSTi89vkPSJaXHP5H0NUmHSDojlR0maedSnUmShmRqz3/niGNmZmZmZma2pjmh0oHSMs31ZBqwH0Ba4WdjoLzk8n4USyCPj4hzU9lhwM50DCdUzMzMzMzM1nbdOvhWp+q4aZ1L0jcknZbu/0zS3en++yT9XtKnJM2VNE/Sj0vbvS7pe5KmA/uWyteXdLukE9PjYyXNkTRb0pVV9n+ipBnp+RskbZDKj0r7nC1pSirbRdIDkmalmNvXOKyppIQKRSJlHvCapA0lrQe8E3hY0khJF0naDziEYvnnWZK2Tdselfb3F0kHpDb0lnRZ6pOHJb03lY+U9ObSxZJukTRc0rnA+inuVVWOf7SkKyXdLemJUr/1lTRB0kNpX4em8nMkfaW0/Q+az5+ZmZmZmZlZbk6o1DYFOCDdHwL0ldQT2B94AvgxcBDF/Cd7Szos1e0DzIuIfSLi3lTWF7gZ+ENE/E7SLsCZwEERsQfwZiKgZFxE7J2e/zNwQio/G/hgKj8klZ0M/CIiGlJbn612QGnd7UZJW1EkVu4DmhM/Q4A5EbG0VH8aMB74ekQ0RMST6akeETEUOB34Tir7UtpmN+BTwOWSeldrR6p3BrAoxT2mRrXdgY+m9p0taXNgMXB4RLwLeC/wE0kC/gf4HLw5+uaTwFsSNen5kyTNlDTzrzffXKuJZmZmZmZm1hZSx97qlBMqtT0I7CWpH7CEIvkwhCLJ8gowKSL+ExGNFF/ch6XtmoAbKmL9EbgsIq5Ijw8Cro+I+QAR8VKV/e8q6R5Jc4FjWHFpzlRgbBqx0XxJ0X3Af0v6JrB1RCxq4biaR6k0J1TuKz2e1sJ2ZePSzweBwen+/sCV6XgeA/4G7NDGeLX8MSIWpX6aCAwFBPxQ0hzgT8AWwKYR8QzwoqQ9gQ8AD9daJSkixkTEkIgYst3HP76aTTQzMzMzM7OuyAmVGiJiGfAMcBxFouEeihER2wJ/b2HTxRHRVFE2FfhwGkkBRVIgWmnCWODUNOLju0Dv1K6TgW8DWwKzJA2KiD9QjFZZBNwh6aAW4jbPo7IbxSU/91OMANkvtbMtlqSfTaxYKapW2rCRlV9nVUetSPpSuvxnVhqJAm/to6BILr0N2CuNyHmhFPMSYCTFObu0TUdiZmZmZmZmq8cjVKyKKcCo9PMeiktrZlEkIQ6UtHGaePZTwOQW4pwNvAj8Oj2eABwtaRCApI2qbNMP+Ge6zOjNS2IkbRsR0yPibGA+sKWkbYCnIuJCikt0dm+hLVOBjwEvRURTGh0zkCKpcl+V+q+ltrRmSnM7Je0AbAU8TpGUapDUTdKWFKNMmi1Lx0dE/Cpd/tOQLk0CODTNzTIIGA7MAAYA/46IZWmelq1L8W4EPgTsDdzRhjabmZmZmZmZrRInVFp2D7AZcF9EvEAxf8c9EfFP4FsUl6HMBh6KiD+2Eut0oLek8yLiEeAHwGRJs4GfVql/FsX8JncBj5XKz2+eDJciiTEbGAHMkzQL2Am4gtrmUqzuc39F2YLmS5AqXAN8PU00u22V55v9GuieLlG6FhgZEUsoEjhPp31cADxU2mYMMKfapLTJA8Ctqa3npETLVcAQSTMpEjhv9k2a/2Ui8L9VRgmZmZmZmZlZR+iiq/woorUrT8zWPEmjgdcj4oJ2bNONImFzVEQ80ZZtPj1pctY3QK/Mb/blmd+evbrnDdi4PO/wu2s/c3HWeEddeXLWeLktzxyvR+bRkC8vzfuCHtAz9xF3Pd0yn+PcnzG5NdZ5+3K/5+pdR7yD6/hvZDOzDnfFgQeuM79Jdtj/4g79rf2Xe0+uy77q0XoVs/onaWfgFuDGtiZTAHrXeYIhtzca87Yv95eJ3AmQ6z6bN0FzzB++kDVe7i+zyyPvCdmwV96vT/X+5R3yf2HM/WVxbejDnHInqV9ckjlJmPk9Uu/JhY5oX49uXev3sJnZuirqeJ6TjuSEyjoozTkyocpT76u18k29iYjR7az/KLBNx7TGzMzMzMzMbGVOqKyDUtKkobPbYWZmZmZmZl1A1xygUvejS83MzMzMzMzM6k5dJlQkbS7p+nS/QdJH2rDNcEm3tGMfh6V5N7LUaw9JIenK0uMekv7TnvbXiPtmv7Vjm7GSjmzh+T9Kuq+ibLSk5yTNSrdzV7XNZmZmZmZmtpbrpo691am6S6hI6hERz0dE85f8BqDVhMoqOAxoS6KkrfXaYyGwq6T10+P3A8+1J4CkHpWPK/pttUkaCLwLGCjpHRVP/ywiGtLtjFz7NDMzMzMzM1sbZEuoSBos6TFJl0iaJ+kqSQdLmirpCUlD022apIfTzx3TtiMlXSfpZuDOFGuepF7A94ARaSTEiFoxWmnbuZIelTRH0gWS9gMOAc5PcbeVdKKkGZJmS7pB0gY16k2SNCTF3VjSM+n+LpIeSPXmSNq+lWb9H/DRdP9TwNWl9ra1n6r2W6rbXdL56ZjmSPpCKpeki1J/3Aps0kIbjwBuBq4BPtlaP5dJ2kzSlNQf8yQdkMpfl/QTSQ9JmiDpbam8QdL9qa03StowlbervyV9plT+W0ndq7TtJEkzJc18fPzN7TksMzMzMzMzqyR17K1O5R6hsh3wC2B3YCfg08D+wCjgv4HHgGERsSdwNvDD0rb7Ap+LiIOaCyJiaap3bRoJcW0rMd5C0kbA4cAuEbE78P2ImAaMB76e4j4JjIuIvSNiD+DPwAk16tVyMvCLiGgAhgDPttJX1wCflNQ79df00nPt6ae39FtyArAgIvYG9gZOTKNMDgd2BHYDTgT2a6GNzYmeq9P9sq+WLvn5YJVtPw3ckfpjD2BWKu8DPBQR7wImA99J5VcA30znaG6pvJa39LekdwIjgPek8ibgmMoNI2JMRAyJiCE7HvLxVnZjZmZmZmZm9la5V/l5OiLmAkh6BJgQESFpLjAYGABcnkYTBNCztO1dEfFSG/bRUoxqXgUWA5ekERm15inZVdL3gYFAX+CONrSl7D7gTElvp0jOPNFS5YiYI2kwRaLitoqn29NPtfrtA8DuWjE/ygBge2AYcHVENAHPS7q7WvskbUqRILs3ncNGSbtGxLxU5WcRcUELhzgDuFRST+CmiJiVypcD16b7vwfGSRoADIyIyan8cuC6FmJDlf6W9D5gL2CGiizm+sC/W4ljZmZmZmZmq6N+B5F0qNwjVJaU7i8vPV5Okbw5B5gYEbsCHwd6l+ovbOM+WooBgKQ70siJSyKiERgK3EAxH8rtNeKOBU6NiN2A71aLmzSyot/erBMRf6C4PGgRcIekyhEj1YwHLqB0uU/Snn6q1W8Cvlya5+QdEXFnc3PfUlnapzTi5BCKkR4bAk+ny2wG08JlP5XbR8QUiuTNc8CVko6tselb2lKhPf0t4PLSMe8YEaNbiW9mZmZmZmarw5PSrhEDWDH56sg2bvMa0K89MSLig+kL9ecl9QUGRMRtwOkUk9xWi9sP+GcaUVG+TKSy3jMUoyAA3pwAVtI2wFMRcSFFomT3NhzbpcD3mkf1lKxKP1W6AzglHQ+SdpDUB5hCcalRd0mbAe8FiIjppUTEeIqRMx+KiMERMZjimGsmVCq3l7Q18O+I+B3wPxST20Lxmmvut09TjIBZALzcPM8K8FmKy4Ggff09AThS0iapzkapHWZmZmZmZmZZremEynnAjyRNBd4yWWgNE4Gd08iHEasQox9wi6Q5FF/Sv5rKrwG+niZ+3RY4i2Iek7so5jChRr0LKBIV04CNS/VGAPMkzaKYP+aK1hoWEc9GxC+qPLUq/VTpEuBR4KE0Ue1vKUYJ3Qg8QTFPyW9Ykbh4U7oUaSvg/lJbnwZelbRPG/c/HJgl6WGKyW2bj3MhsIukB4GDKCYdBvgcxeS/cyiSXs3lbe7viHgU+DbFBL1zKM7lZm1sr5mZmZmZma2KLjoprSJau+LCLB9Jr0dE385uR7N3/Gpy1jfAztuvav6rujca8354vPji8qzxNtwwb052636NWeP16p738+2qT/82a7z/PHli1ngfv2u9rPE2Wi/v62WLDfKeX4D5S/K+BvfeeGnWeE++lneqsn8tyvsZ079nfZ/jQ7de0nqldjjutv5Z4+2xdd7+27bfsqzx/vZ6a9PMtc8zmV/PAH+b8nLWeHt/KO857t0t7++R3KPWl2f+Mz53+xY35Q3YI/P5yC33f6o74iqHpXk/tuhRv99zgfp/z11x4IF13oNtt/37/6dD36BP3HVCXfZV/t+MZmZmZmZmZtZ11GW6o+M5odJBJA2imNOj0vsi4sU13Z56UU+jU8zMzMzMzMxWlRMqHSQlTRo6ux1mZmZmZmZmHaqOV+LpSGt6UtoWSdpc0vXpfoOkj7Rhm+GSbmnHPg6TtHOueu0hKSRdWXrcQ9J/2tP+GnHf7Ld2bDNW0pGt13yz/vckHdz+1pmZmZmZmZmte+omoSKpR0Q8HxHNX/IbgFYTKqvgMKAtiZK21muPhcCuktZPj9/PiuWR20RSj8rHFf3WISLi7Ij4U0fuw8zMzMzMzNZC6uBbnVrthIqkwZIek3SJpHmSrpJ0sKSpkp6QNDTdpqWlh6dJ2jFtO1LSdZJupljqdnCK0Yti2dwRzcsl14rRStvOlfSopDmSLpC0H3AIxfK8syRtK+lESTMkzZZ0g6QNatSbJGlIiruxpGfS/V0kPZDqzZG0fSvN+j/go+n+p4CrS+1taz9V7bdUt7uk89MxzZH0hVQuSRel/rgV2KSFfvuGpLmpT85NZWMlHSnpw5L+t1R3eGpHZYyj0rmcLWlK6Tj+KOl2SY9L+k6p/tdS/XmSTk9lbx5XejxK0uh0/7TSub0mlfWRdGk69oclHdrKuTAzMzMzMzNbJbnmUNkOOAo4CZgBfBrYnyIp8d/AscCwiGhMl438EDgibbsvsHtEvCRpMEBELJV0NjAkIk4FkNS/hRhvIWkj4HBgp4gISQMj4hVJ44FbIqL50qJXIuJ36f73gRMi4pdV6tXa1cnALyLiqpQIam1Ny2uAs1Vc5rM7cClwQHrusTb208hq/ZacACyIiL0lrQdMlXQnsCewI7AbsCnwaNp3Zb99mGJ0zj4R8Ubqx7K7gN9K6hMRC4ERwLVVjvNs4IMR8ZykgaXyocCuwBvAjJTcCeA4YB+K/ON0SZOBltZSPAN4R0QsKcU/E7g7Io5PZQ9I+lNqZ/kYT6J4rTLok/+Pfvt/vIXdmJmZmZmZWUui9vfldVquhMrTETEXQNIjwISUxJgLDAYGAJen0RsB9Cxte1dEvNSGfbQUo5pXgcXAJelLe615SnZNiZSBQF/gjja0pew+4ExJbwfGRcQTLVWOiDkpAfIp4LaKp9vTT7X67QPA7loxP8oAYHtgGHB1RDQBz0u6u0YTDwYui4g3UntX2kdK9twOfFzFvC0fBb5RJc5UYGwazTKuot0vAkgaR5F4C+DG5sRHKj8AGF+jjQBzgKsk3QTcVDr2QySNSo97A1sBf644hjHAGIB3/Gpyh66XbmZmZmZmZuumXHOoLCndX156vJwiaXMOMDEidgU+TvFFt9lKowda0FIMACTdkS69uSQiGilGQ9xAMeLi9hpxxwKnRsRuwHerxU0aWdFfb9aJiD9QjMRZBNwh6aA2HMt44AJKl/sk7emnWv0m4MsR0ZBu74iIO5ub+5bK0j6pz2ZJOiRt31qS4VrgaOAgYEZEvCbpB81xACLiZODbwJbALBXLSFdrQ1D7qrhyn8PK/fFR4FfAXsCDKuaWEXBE6di3ioiVkilmZmZmZmaWWTd17K1OralJaQewYvLVkW3c5jWgX3tiRMQH0xfpz0vqCwyIiNuA01mxhHFl3H7APyX1BI5pYf/PUHx5B3hzAlhJ2wBPRcSFFImS3dtwbJcC32se1VOyKv1U6Q7glHQ8SNpBUh9gCvDJNMfKZsB7ASJieikBMR64Ezhe0gZp+8pLfgAmAe8CTiRd7hMRZzbHSdttm2KfDcynSKwAvF/SRiom5j2MYiTLFOAwFfPX9KG4VOse4AVgE0mD0uVLH0uxuwFbRsREitExA1kxuujLStdnSdpzFfvQzMzMzMzMrEVrKqFyHvAjSVNpfY6RZhOBndOohxGrEKMfcIukOcBk4Kup/Brg62nS0m2Bs4DpFHODPFbavrLeBRSJimnAxqV6I4B5aWTGTsAVrTUsIp6NiF9UeWpV+qnSJRTzozyUJnT9LcUooRuBJ4C5wG8o+qRa226nSAzNTMc0qkqdJopLqD5M7Uupzlcxse08ioTJ7FR+L3AlMAu4ISJmRsRDFCOFHqA4F5dExMMRsYxicuLpaT/N56c78Pt0SdnDwM8i4hWKET49gTlpv+e01FFmZmZmZmaWQRdd5UcRnkLC1ow0me6bEw3Xg38tGp/1DfDtB/vnDMdGvZZnjbdd/2VZ4/3t9VzTMKV4C/PG69Ut7+fbhe/Oez7etu3vssY7fdzns8Z7YdGq5nWr+8fC1qa+ar+t++Z9TT/+Ut7X4Jb9m7LG690972t6cVPev1Cez/we/vttL2SNd9JJ/Vqv1A7zXumVNd7LS/K+5zZcL+/rb+PM8QDe0a8xa7y5L+c9J0szv0fqXWPmrwW5P7Mal+c9H3l/q6+5/1Svjh6Z/zbKfU7qXe7XzO8PPHCd6cDtPj62QxMLf715ZF32Vd6/fMzMzMzMzMysa/EqP5ZDmnx1QpWn3te8uk1XFRFjKS7tMTMzMzMzM1urOaGSWUqaNHR2O8zMzMzMzMzWiDpeiacjrQ2X+mUhaXNJ16f7DZI+0oZthkuqNelqtfqHSdo5V732kBSSflJ6PErS6Jz76EySDpF0Rju3eUbSxq3XNDMzMzMzM2ufLpFQkdQjIp6PiObljhuAVhMqq+AwoC2JkrbWa48lwCfWVAJBUoeNbqqMnc7f+Ig4t6P2aWZmZmZmZquoi67yU9eX/EgaDNxOsdTuuymW3r0M+C6wCXBMqvpzYH1gEXBcRDyeVpT5KNAb6CPpeIqld99FsRTv+pL2B34EPF0tRittOxc4BGgE7gTGpccHSvo2cARwEHAS0Av4K/BZimROZb3/AUZFxMyUEJkZEYMl7ZKOtxdF8uuIiHiiRpMagTEUy0OfWdHWtwEXA1ulotOB+4CngIa05DCS/gq8h2IC65XqR8TUNOJlc2AwMB/4dGkfIykSRd2BXYGfpHZ/liLZ85GIeEnSiZV9EhFvSBoLvATsSbHk86CKx3NJKwRVO57UvkHA1cDbKJZgruO3npmZmZmZma3N1oYRKtsBvwB2B3ai+BK/PzAK+G/gMWBYROwJnA38sLTtvsDnIuKg5oKIWJrqXRsRDRFxbSsx3kLSRsDhwC4RsTvw/YiYBowHvp7iPgmMi4i9I2IP4M/ACTXq1XIy8IuIaACGAM+20le/Ao6RNKCi/BfAzyJib4oEziURsRz4YzoOJO0DPBMRL1SrX4q1F3BoRHyat9qV4vwMBX4AvJH69D7g2FTnLX1S2n4H4OCI+H81Htc8nlT+HeDetM/xrEi4rETSSZJmSpp55f/cUa2KmZmZmZmZtZXUsbc6VdcjVJKnI2IugKRHgAkREWnEwmBgAHC5pO2BAHqWtr0rIl5qwz5ailHNq8Bi4BJJt1KMfKlmV0nfBwYCfYH2fnu/DzhT0tspEhG1RqcAEBGvSroCOI1ipE2zg4GdteKF2F9SP+BaigTSZcAn0+OW6gOMj4hy7LKJEfEa8JqkBcDNqXwuRUIMWu6T6yKiqYXHrR3PMOATqS9ulfRytUZGxBiK0Tz8a9H4Dl0v3czMzMzMbJ1XB0kPSR+i+Od7d4pBBG+ZMkLScIqrU3oC8yPiwNXZ59owQmVJ6f7y0uPlFAmhcyi+yO8KfJziEp9mC9u4j5ZiACDpDkmzJF0SEY0UozBuoLjM5fYacccCp0bEbhSXKb0lbtLIinPxZp2I+APF5UGLgDskHVRl20o/pxj10adU1g3YN42IaYiILVLi4z5gu3QJzWEUly21VB9Sn0o6PPXHLElD0nOtnStouU8qz1et89dS+5wgMTMzMzMz60Ikdae4YuPDFPOVfqpyIRhJA4FfA4dExC7AUau737UhodKaAcBz6f7INm7zGtCv9LjVGBHxwfTl/fOS+gIDIuI2ivlIGmrE7Qf8U1JPVsz3Uq3eMxSX0gA0T5yLpG2ApyLiQopLWHanFWlEzv+y8qU0dwKnluI2pLoB3Aj8FPhzWvK5Zv2K/dxYSmjMbK1dJbX6pD1qtW9Kc0xJHwY2XMX4ZmZmZmZm1lbdOvjWuqHAXyPiqTTNxzXAoRV1Pk1x5cffASLi36t0rCXrQkLlPOBHkqZSDO1pi4kUl4zMkjRiFWL0A26RNAeYTDERLBQn7euSHpa0LXAWMB24i2KeFmrUuwA4RdI0oLxKzwhgnqRZFPPHXNHG4/tJRZzTgCGS5kh6lGJulmbXAp9hxeU+rdVfXbX6pD1qte+7wDBJDwEfAP6+uo01MzMzMzOzurcF8I/S42dTWdkOwIaSJkl6UNKxrCYVgxTMuqbht07N+gZ4dcHynOEYtFHenOeSZVnD8fZ+jVnj9arzFO/fXs877dS7N1mcNd7PP3FJ65Xa4ZTrTswar0cHXFrbs1ve32Hrd88b7+8L875mtumX903878Vt/T9E2yxuynuSB/TK+5n69GutTZHWPr0yv/76Zz7eNxrzv+leXJL3NbNe5vdc78znpFvnTwnQom7Ke7yNy+v7gHOfjw165H3P5f4MXB71fT4AGuv8q2TuPy1zvwbHDjuw/k9yG2139FUd+mp48rrPfIFitdhmY9LcmABIOgr4YER8Pj3+LDA0Ir5cqnMRxWIv76NY4fc+4KMR8ZdVbdfaMCmtmZmZmXWy3MkUMzOztiovLFLDs8CWpcdvB56vUmd+RCwEFkqaAuwBOKHSFUgaBEyo8tT7SvOfmJmZmZmZma05nT/WZgawvaR3UMyP+kmKOVPK/ghcJKkH0AvYB/jZ6uzUCZW1SEqaNHR2O8zMzMzMzMzqRUQ0SjoVuINiXtRLI+IRSSen5y+OiD9Luh2YQ7ES7SURMW919uuEyipIq8psnlb5QdIhwM7V1rlexfiDgT8Dj5eKfxoRbZ2Udp0maXPgwog4soU6g4Fb0lLYZmZmZmZm1kGiDiadSt/Pb6sou7ji8fnA+bn26YTKqmmgmMzmNoCIGE+xrHFOT0ZEQ+aYaz1JPSLieUrLS5uZmZmZmZmtaXW+psbKJH1N0rx0Oz2VHZuWz50t6cpUtqmkG1PZbEn7SRosaV4p1ihJo9P9SZJ+Lmlaij00lQ9NZQ+nnztK6gV8DxjRvOyypJFpxmAkbS1pQmrTBElbpfKxki5McZ6S1O6EQIr9hKSNJXWTdI+kD0jqLul8STPSfr9Q2uYbkuamfnjLCJrUL49JuiQd+1WSDpY0Ne2rZl+k8pGSxkm6PdU/rxT7N5JmSnpE0ndL5R9J+7w39cktqbyPpEvTcTws6dDSPq6TdDNwZ/lcpvv3SHoo3fZrb7+amZmZmZnZapA69lan1poRKpL2Ao6jmDhGwHRJM4AzgfdExHxJG6XqFwKTI+JwSd2BvsCGreyiT0TsJ2kYcCmwK/AYMCxdj3Uw8MOIOELS2cCQiDg1tW1kKc5FwBURcbmk41NbDkvPbQbsD+xEMaLl+hbas62kWaXHX46IeyT9GLgYmA48GhF3SjoJWBARe0taD5gq6c60n8OAfSLijVL/VNoOOIpiGaoZFJP37A8cAvx3ivGWvgCOSNs3AHsCS4DHJf0yIv4BnBkRL6VzMEHS7hQzKP82xXpa0tWldpwJ3B0Rx0saCDwg6U/puX2B3VO8waVt/g28PyIWS9oeuJpi9FBNqb9OAtj+1K+z+YcObam6mZmZmZmZ2VusNQkVii/4N6YljpA0juKL8/URMR8gIl5KdQ8Cjk1lTcACSa0lVK5O9adI6p++0PcDLk9f1APo2YZ27gt8It2/Ejiv9NxNEbEceFTSpq3EqXrJT0RcomKN7ZNZMUHtB4DdS6NeBgDbAwcDl0XEG2nbl6ju6YiYCyDpEWBCRISkucDgUsxafTEhIhak7R8Ftgb+ARydkhc9KJJJO1OMinoqIp5O217NivXEPwAcImlUetwb2Crdv6tG+3tSzNTcADQBO9Q4xjeVl9wafuvUDl0v3czMzMzMbJ1Xv4NIOtTalFCpdooi3dqikZUvcepdJVbl43OAiWmky2BgUhv3VSvuktL9VXrJSdqAYk1tKEbevJZifTki7qio+6GK/SNpS+Dm9PBi4PaKdi0vPV7OitdIS31R3r4J6KFiuapRwN4R8bKksRR93tJxCzgiIh5fqVDaB1hYY5uvAi9QrB/eDVjcQnwzMzMzMzOzLNamOVSmAIdJ2kBSH+Bw4EGKURCDAEqXtEwATkll3SX1p/jSvYmkQemymI9VxB+R6u9PcfnMAopRGc+l50eW6r5GMXqlmmkUa14DHAPcuwrH2pIfA1cBZwO/S2V3AKdI6gkgaYfUR3cCx6ckDJI2ioh/RERDul1cJX4ttfqilv4USZAFaTTOh1P5Y8A2pct2RpS2uQP4slRcJCdpzza2659p5M9nKZbIMjMzMzMzszWlmzr2VqfWmoRKRDwEjAUeoJg/5JKImAr8AJgsaTbw01T9K8B70yUrDwK7RMQyislkpwO3UHyxL3tZ0jSKURsnpLLzgB9JmsrKX9QnAjsrTUpbEec04DhJcyi+4H9lFQ952xS/+XaapAOBvYEfR8RVwFJJxwGXAI8CD6XJWn8L9IiI2ynmapmZ5mMZVX1XbVKrL6qKiNnAw8AjFHPSTE3li4AvArdLupci0bUgbXYOxSU8c9JxnNOGdv0a+Jyk+yku96k1ksXMzMzMzMwsG0V4CglJk4BRETGzs9vSFUjqGxGvp5EovwKeiIifdUZbcs+h8uqC5TnDMWijvDnPJcuyhuPt/RqzxutV5ynev72e9yrJd2+S9wq1n3/ikqzxTrnuxKzxenTAPxd6dsv7O2z97nnj/X1h3tfMNv3yvon/vTjvoL7FTXlP8oBeeT9Tn36tLVOhtV2vzK+//pmP943GvOfjxSX5B4Gul/k91zvzOanjf4oC0E15j7dxeX0fcO7zsUGPvO+53J+By6O+zwdAY51/lcz9p2Xu1+DYYQfW/0luo22PvbZDXw1PXjGiLvtqbZpDxdYdJ0r6HNCLYhTLbzurIYN65/1FesB/LWm9Ujv8Y2HeP/7/uSjvH8Ozn877ubbdlnnjbZj5y8lG6+WN90Lm85E7AfKbo37XeqV2OO6ak1qv1E7dMi+jtzDzX4a5/9DMnQDJ/YX79ca8f7q+vixvvP49876Hc5/fxrzNY2nmL8e5E5gA/8n8Obh137yJ/uWZDzn3l7F6/8Kd/3jzxnsyc5J1s/WbssbrCNlf03nD1f1rJnc8W/s5oQJExPDO2K+k3ShWAipbEhH7dEZ71pQ0GqVTRqSYmZmZmZlZZvWd3+0wTqh0orRUcUNnt8PMzMzMzMzM2scJFTMzMzMzMzNbdfU+6VQHqfMpINcukhokfaT0+BBJZ2SMP1jSoorVf3rlip/2cbKkYzPGu1rSHElflfQ9SQe3UHespCOrlG8u6fpcbTIzMzMzM7OMuuiyyR6hklcDMAS4DSAixlMsW5zTkxHR0J4NJHWPiDbNkhURF69Sq6rv97+A/SJi69WJExHPA29JtJiZmZmZmZl1lnVihIqkr0mal26np7Jj08iI2ZKuTGWbSroxlc2WtF8a9TGvFGuUpNHp/iRJP5c0LcUemsqHprKH088d00iR7wEj0siREZJGSroobbO1pAmpTRMkbZXKx0q6MMV5qtoIjVaOfbCkeyQ9lG77pfLhkiZK+gMwNz2eLOl/Jf1F0rmSjpH0gKS5krZN242WNKp0/D9Odf4i6YBUvkGKM0fStZKmSxpSpXl3Apuk/jigPAIl7f/RFOOC0jbDKvuifI5Sn46TdLukJySdV+qLE1I7J0n6XXPfV+mzkyTNlDTz6Vty57vMzMzMzMy6llDH3urVWj9CRdJewHHAPhRzC0+XNAM4E3hPRMyXtFGqfiEwOSIOl9Qd6Ats2Mou+kTEfpKGAZcCuwKPAcMiojFdwvLDiDhC0tnAkIg4NbVtZCnORcAVEXG5pONTWw5Lz20G7A/sRDGipaXLW7aVNCvdnwp8HXh/RCyWtD1wNcUoGYChwK4R8bSk4cAewDuBl4CngEsiYqikrwBfBk6vsr8eqc5HgO8ABwNfBF6OiN0l7QrMqrIdwCHALc0jaiSdkH5uBBwO7BQRIWlgaZu29EUDsCewBHhc0i+BJuAs4F3Aa8DdwOxqjYqIMcAYgCMm3OPFz8zMzMzMzKzd1vqECsWX7xsjYiGApHEUCYXrI2I+QES8lOoeBBybypqABZJaS6hcnepPkdQ/ffnvB1yeEhgBtGUR+32BT6T7VwLnlZ67KSKWA49K2rSVOCtd8iNpAHCRpAaKpMIOpboPRMTTpcczIuKfabsnKUaQAMwF3ltjf+PSzweBwen+/sAvACJinqQ5rbS50qvAYuASSbcCt5Sea0tfTIiIBek4HgW2BjamSJa9lMqvY+W+MDMzMzMzs45Qx/OcdKR14ZKfamcu0q0tGlm5H3pXiVX5+BxgYkTsCny8yjZtUY67pHS/va/ErwIvUIw+GQKUJ6ldWFG3vJ/lpcfLqZ1ca67TVKpTtY2SDteKyXKrXQIEQEQ0UoyeuYFilM7tNdpYqy/KdZrb1TXfwWZmZmZmZtYp1oWEyhTgsDSvRx+KS0keBI6WNAjevMQEYAJwSirrLqk/RTJiE0mDJK0HfKwi/ohUf39gQRoZMQB4Lj0/slT3NYrRK9VMAz6Z7h8D3LsKx1rNAOCfaVTHZ4HumeK25F7gaABJOwO7AUTEjRHRkG4za20sqS8wICJuo7jMqCFDmx4ADpS0oaQewBEZYpqZmZmZmVlrpI691am1PqESEQ8BYym+UE+nmBdkKvADYLKk2cBPU/WvAO+VNJci6bJLRCyjmEx2OsWlJ49V7OJlSdOAi4ETUtl5wI8kTWXlBMZEYOc0QmNERZzTgOPS5TGfTW3J4dfA5yTdT3GJS+WolI7wa+Bt6Vi+CcwBFrRj+37ALWn7yRSjbFZLRDwH/JDiPP4JeLSdbTIzMzMzMzNrM0V4Ts5aJE0CRrU02qIrShP69kwT4W5LMfJnh4hY2snt6hsRr6cRKjcCl0bEjS1t862ZE7K+Afr1WJ4zHH9fmHeao151nkJ9ZWl9N3Bgr7zn988LerVeqR323GhJ65Xa4fXGvP8NuOyTY7LGAzjx2pOyxuvVLe/vxJcyv6Y3Xi/va/DVZXnP8QuL8n5mvbosb/9t3z/vr6mly/P23+uNeY+3d+bXc4/M8QD+9npbpqFruy37NGaNtzzzITdmjtejfv9pCxTXlOeU+3hfzvwZ3a9n7iPOL/drOvc5qfdpOHK/h39/4IF1fsRtt80p4zo0sfDUbz5Rl321LkxKa2veBsBEST0p5i45pbOTKcnotOpSb4oJd2/q3OaYmZmZmZnZusoJlRZExPDO2K+k3ShWAipbEhH7dEZ7KkXEa6xYmrluRMSozm6DmZmZmZlZl1PfA807jBMqdSgi5pJnolYzMzMzMzMz6wBdNI/UOkkNkj5SenyIpDMyxh8saV5F2WhJWUZZSBor6ch0f1JLyxjnJGm4pFvWxL7MzMzMzMysDniVH6vQALyZUImI8RFxbuc1Z81Jk86uddJktGZmZmZmZmYdru4SKpK+Jmleup2eyo6VNEfSbElXprJNJd2YymZL2q9y1IekUZJGp/uTJP1c0rQUe2gqH5rKHk4/d5TUi2Ip5RHNSyBLGinporTN1pImpDZNkLRVKh8r6cIU56nmESKr2A8nSpqRju0GSRu0tA8VLpL0qKRbgU1qxP2ApPskPSTpOkl9U/kzks6WdC9wlKTTUqw5kq6pEWvv1I7Zkh6Q1K/i+T6SLk3H8bCkQ1P5YEn3pDY8JGm/VD48nafrJT0m6SqpSEdK2kvSZEkPSrpD0mal8/pDSZOBr0g6Kp3f2ZKm1Gj3SZJmSpo5a5wH05iZmZmZma2WburYW52qq//oS9oLOA7Yh2L1mOmSZgBnAu+JiPmSNkrVLwQmR8ThaURFX2DDVnbRJyL2kzQMuBTYFXgMGBYRjSpWiPlhRBwh6WxgSEScmto2shTnIuCKiLhc0vGpLYel5zYD9gd2AsYD17fQnm0lzSo9/i/ggnR/XET8Lu37+8AJwC9b2MfhwI7AbsCmwKPpGN8kaWPg28DBEbFQ0jeBr1EkjwAWR8T+qe7zwDsiYomkgZUNT0mna4ERETFDUn9gUUW1M4G7I+L4FOMBSX8C/g28Py27vD1wNSsmud0T2AV4HpgKvEfS9HTsh0bEfySNAH4AHJ+2GRgRB6Z2zQU+GBHPVWs3QESMAcZA/mWTzczMzMzMrGuoq4QKRZLgxohYCCBpHMUX7esjYj5ARLyU6h4EHJvKmoAFklpLqFyd6k+R1D994e4HXJ6+2AfQsw3t3Bf4RLp/JXBe6bmbImI58KikTVuJ82RENDQ/UBpNk+yaEikDKZJFd7Syj2HA1akvnpd0d5X9vRvYGZiaBn70Au4rPX9t6f4c4CpJN1F9+eEdgX9GxAyAiHg1HUO5zgeAQ7RiXpjewFYUyZKLJDUATcAOpW0eiIhnU6xZwGDgFYrk110pfnfgnzXaPRUYK+l/gXFV2m1mZmZmZmYZRR3Pc9KR6i2hUu0sRLq1RSMrX8bUu0qsysfnABPTSJfBwKQ27qtW3CWl+6vzqhoLHBYRs9PomOFt2Edr/STgroj4VI3nF5buf5QiSXMIcJakXYBbKUa/zKQYldOW/R0REY+vVFgkjl4A9qA4X4tLT5ePrYniNSrgkYjYt7V2R8TJkvZJ7Z8lqSEiXmylnWZmZmZmZmbtUm9zqEwBDpO0gaQ+FJexPAgcLWkQQOmSnwnAKamse7rk5AVgE0mDJK0HfKwi/ohUf39gQUQsAAYAz6XnR5bqvkYxeqWaacAn0/1jgHtX4Vhb0w/4p6SeaR+tmQJ8MvXFZsB7q9S5n+ISmu0AUj/vUFlJUjdgy4iYCHyDNEomIj4YEQ0R8XmKS6U2l7R32qaf3jop7B3Al0vzoOyZygdQjG5ZDnyWYsRJSx4H3iZp3xSnZ0rwvIWkbSNiekScDcwHtmwltpmZmZmZma2Obh18q1N11bSIeIhiZMYDwHTgkoiYSjFfxmRJs4GfpupfAd6b5sx4ENglIpZRzAcyHbiF4kt/2cuSpgEXU8xJAsXlOj+SNJWVv9hPBHZWmpS2Is5pwHGS5lAkBL6yekde1VkUx3EXbz2Oam4EngDmAr8BJldWiIj/UCSNrk5tv59iHpZK3YHfp759GPhZRLxSEWspRYLql+m83MVbRwSdQ3EJ1RwVkwWfk8p/DXxO0v0Ul/sspAVpX0cCP077mgXsV6P6+ZLmpv1NAWa3FNvMzMzMzMxsVSiia8zJKWkSMCoiZnZ2W6x+fP7eSVnfALsMXJYzHI8vyHtV3huNeXOoA3stzxrvlaV1leN9i8bMH5cb9MgbcNPeTVnjLViW93x0xNn93YgxWeOddsPns8Z7cUneVejr/Ry/tCRvvF71/ZGQ3WuZz0fPbnk/Ywb2yvv6A5if+T3Su3veY166PO+cAE2Z463XPe/v4TpeSAOAxU15G7gs8/no06P+z0fuPtwg83su9zEvz/y3W+6/Bf8w/MA6f9e13Tv+3/gOTSw8/ZND6rKv6m0OFTMzMzMzMzNbm3hS2nVbRAzvjP1K2o1iJaCyJRGxT2e0x8zMzMzMzMxWX5dJqHSWiJgLNHR2O8zMzMzMzMw6RL1fM9hButjVyW0jqUHSR0qPD5F0Rsb4gyWFpHNKZRtLWibpolWM+XqN8pMlHbuqbc1pTbWl8vyZmZmZmZmZ5eYRKtU1AEOA2wAiYjwwPvM+nqJY1vms9Pgo4JHM+yAiLs4dc1WtwbY0UDp/ZmZmZmZm1oG65gCV+hqhIulrkual2+mp7FhJcyTNlnRlKttU0o2pbLak/dKoj3mlWKMkjU73J0n6uaRpKfbQVD40lT2cfu4oqRfF0ssjmpdMljSyeeSIpK0lTUhtmiBpq1Q+VtKFKc5Tko5s5XAXAX+WNCQ9HgH8b6n9H5c0PbXtT5I2TeV9JV2WlgaeI+mI0jY/SP1xf6n+aEmjSv3wY0kPSPqLpANSeXdJ50uakWJ+ocb52UjSTanO/ZJ2L+3jSkl3S3pC0ok1tm9LW3ZJZbPSfrZP5/YxSZensuslbZDq7536fHbabkDl+WvlPJiZmZmZmZm1W90kVCTtBRwH7AO8GzhR0nuAM4GDImIP4Cup+oXA5FT2Lto2sqNPROwHfBG4NJU9BgyLiD2Bs4EfRsTSdP/aiGiIiGsr4lwEXBERuwNXpbY02wzYn2LkybltaNM1wCclvR1oAp4vPXcv8O7UtmuAb6Tys4AFEbFbasPdzccH3J/6ZApQNakB9IiIocDpwHdS2Qkp5t7A3hR9/44q234XeDjt97+BK0rP7Q58FNgXOFvS5m04/mptORn4RUQ0UIwyeTaV7wiMSft+FfhiSn5dC3wlHffBwEJaPn9IOknSTEkzHxt/cxuaaWZmZmZmZrVEN3XorV7V0yU/+wM3RsRCAEnjKL5QXx8R8wEi4qVU9yDg2FTWBCyQtGEr8a9O9adI6i9pINAPuFzS9kAAPdvQzn2BT6T7VwLnlZ67KSKWA482jxBpxe3AOcALFImBsrcD10raDOgFPJ3KDwY+2VwpIl5Od5cCt6T7DwLvr7HPcaU6g9P9DwC7l0bVDAC2L+2z2f7AEWm/d0salEaEAPwxIhYBiyRNBIYCN9VoQ0ttuQ84MyWZxkXEEyqW4PpHRExNdX4PnAbcAfwzImakNr0KoFaW7IqIMcAYgM/fO6lD10s3MzMzMzOzdVPdjFCh+lVXkW5t0cjKx9O7SqzKx+cAEyNiV+DjVbZpi3LcJaX7rabR0miYB4H/B9xQ8fQvgYsiYjfgC6W2iep9siwimsubqJ0sW1KljoAvpxEdDRHxjoi4M11CNEvSrBaOKSp+vlleZftW2xIRfwAOobgk6g5JB9WKT+2+MDMzMzMzszWlmzr2VqfqKaEyBThM0gaS+gCHUyQbjpY0CIo5PFLdCcApqay7pP4Uozw2SaMm1qO47KZsRKq/P8XlLQsoRmI8l54fWar7GsXolWqmsWKEyDEUl+asjp8A34yIFyvKy237XKn8TuDU5gdtGJnTFncAp0jqmWLuIKlPRJzZnGRJ9aZQHDOShgPzm0eFAIdK6p3O1XBgRpXtWyVpG+CpiLiQYiLg3dNTW0naN93/FEW/PwZsLmnvtG0/ST1o+fyZmZmZmZmZrba6SahExEPAWOABYDpwSbrE4wfAZEmzgZ+m6l8B3itpLkXSZZeIWEYxGel0iktfHqvYxcuSpgEXU8wZAsXlOj+SNBXoXqo7Edi5xqSmpwHHSZoDfJYV87qs6nE/EhGXV3lqNHCdpHuA+aXy7wMbqphcdzbw3tXZf3IJ8CjwkIqJfX9L9REuo4Eh6djPZeVEzwPArcD9wDkR8fxbN2+TEcC8NKplJ1bM0/Jn4HNp3xsBv0kjfEYAv0x9cRfFSJ6Wzp+ZmZmZmZnlJHXsrU5pxVUi6y5Jk4BRETGzs9uyLlKxmtLrEXFBB8UfDNySLs3K6pszJmR9A2y+QVPOcDz9Wt5pjrbum7d9f36lLdMOtd3iprwflsuzRoO9Bi3NGm/cU+tnjffBLRdnjTd/Sd6ce48O+F3YvVve32EXHnFJ1njH/KHqommrbHCfxqzxcp/jp1/P+5nw8qK87Xt7v7z9l9uLS7q3XqkdBq2X9zN/8/XzxgPYok/emNP/s17WeD0yf8bklvv35gbd8x5v7t/Duf8T/Ebm/uudu/864OWX+3dxY+Y25r6yI3cf5m7f7w88sH4zBe00+Nv/16EfmM98/8N12Vd1M0LFzMzMzMzMzNZC3Tr41gaSPiTpcUl/lXRGC/X2ltRUWpRlldXTKj8dJiKGd8Z+Je1GsRJQ2ZKI2Kcz2tNRImJ0B8d/Bsg+OsXMzMzMzMzWfpK6A7+iWO32WWCGpPER8WiVej+mmEd0tXWJhEpniYi5QENnt8PMzMzMzMysw3T+PCdDgb9GxFMAkq4BDqWYK7TsyxQr7O6dY6e+5KcTSGqQ9JHS40NaGpK0ivvYRdLdkv4i6QlJZ0nFq1zScEn7leqOzTHcqSNJOlnSsZ3dDjMzMzMzM6s7WwD/KD1+NpW9SdIWFKsJX5xrp06odI4G4M2ESkSMj4hzcwWXtD7FksPnRsQOwB7AfsAXU5Xh6XGOfUlSltdRWvK4qoi4OCKuqPW8mZmZmZmZdZJu6tCbpJMkzSzdTqpoQbUhMpUT5f4c+GZEZJsRvUslVCR9LS03PE/S6ansWElzJM2WdGUq21TSjalstqT9JA1OSwo3xxqVVrdB0iRJP5c0LcUemsqHprKH088dJfWiWN55RPOyvpJGSroobbO1pAmpTRMkbZXKx0q6MMV5qpURJZ8GpkbEnQAR8QZwKnBGWjHnZOCraf8HpG2GVYst6euSZqT2fDeVDZb0Z0m/Bh4CtizV757aOk/SXElfTeXbSrpd0oOS7pG0U+m4fippInC+pGckDSzF+2s6H6MljUpl20n6Uzo3D0natlZbzczMzMzMbO0WEWMiYkjpNqaiyrOUvpcCbweer6gzBLhG0jPAkcCvJR22Ou3qMnOoSNoLOA7YhyJ7NV3SDOBM4D0RMV/SRqn6hcDkiDg8TVrTF9iwlV30iYj9JA0DLqWYRPUxYFhENEo6GPhhRBwh6WxgSEScmto2shTnIuCKiLhc0vGpLYel5zYD9gd2ohiBcn2NtuwCPFguiIgnJfUFXqIY4vTmMseSTqgWW9IHgO0prkcTMD4d39+BHYHjIuKLrKwB2KJ5ieNScmQMcHJEPCFpH+DXwEHpuR2AgyOiKY12ORy4LNV7JiJe0MrX5F1FMfrmRkm9gW612hoRUyo7J2UzTwL4wLdOp+Hwj9XoRjMzMzMzM2tV7jWl228GsL2kdwDPAZ+kGGjwpoh4R/N9SWOBWyLiptXZaZdJqFAkC26MiIUAksZRZKiuj4j5ABHxUqp7EHBsKmsCFkhqLaFydao/RVL/lEjoB1wuaXuK4UY929DOfYFPpPtXAueVnrspIpYDj0ratIUY4q3Dm5rVKq8W+wPp9nB63JciafF34G8RcX+VOE8B20j6JXArcGdK5OwHXFdKjKxX2ua60rCra4Gzgcso3gTXrnRgUj+KhM2NABGxOJXXautbEiopmzkG4JszJnToeulmZmZmZmbWsdIghlMpVu/pDlwaEY9IOjk9n23elLKulFCpdU1VW79QN7LyJVK9q8SqfHwOMDGNdBkMTGrjvmrFXVK631IK8BFgWLlA0jYUo1JeU/UZmKvFFvCjiPhtRazBQHNiqjsrRsOMj4izJe0BfBD4EnA0cDrwSkQ01GjvwtL9+4DtJL2NYmTO9yvq1jruqm01MzMzMzOzjhWdv8oPEXEbcFtFWdVESkSMzLHPrjSHyhTgMEkbSOpDcVnJg8DRkgYBlC75mQCcksq6S+oPvABsImmQpPWAyutERqT6+wMLImIBMIBiuBHAyFLd1yhGr1QzjWJkBsAxwL2rcKxXAfuny4yaJ6m9kBWjXVraf9kdwPFphAmStpC0SblCRDRFREO6nS1pY6BbRNwAnAW8KyJeBZ6WdFSKo5R0eYuICOBG4KfAnyPixYrnXwWebb7WTdJ6kjZoS1vNzMzMzMysA3Tr4FudquOm5RURDwFjgQeA6cAlETEV+AEwWdJsii/xAF8B3itpLkXSZZeIWEYxmex04BaK+VHKXpY0jWJ+khNS2XnAjyRNpRh21GwisHOaFHZERZzTgOMkzQE+m9rS3mNdRLHm9rclPQ7Mpbim7KJU5WbgcK08KW21OHcCfwDuS31xPa0nYrYAJkmaRdHf30rlxwAnpH5+JLWvlmuBz1BxuU/JZ4HTUh9NA/5rFdtqZmZmZmZmtkpUDAiw1SFpEjAqImZ2dlusfXLPobL5BtlW4ALg6dfyXpW3dd+87fvzK22ZFqjtFjflHSq4PGs02GvQ0qzxxj21ftZ4H9xycdZ485fkzbn36ICRoN275f0dduERl2SNd8wfvpA13uA+jVnj5T7HT7+e9zPh5UV52/f2fnn7L7cXl3RvvVI7DFov72f+5uvnjQewRZ+8Maf/Z73WK7VDj8yfMbnl/r25Qfe8x5v793Du/wS/kbn/eufuvw54+eX+XdyYuY255zXN3Ye52/f7Aw/s/OtkMtn6R3/q0A/Mv33r4Lrsq640h4rZW+T+479ped73+StL8/5xvfy1+k5Y5Jb7D68nMye4tuyf94vE3xfmbV/uP5I6Qu4/bHInQK76dN5pnT6buX25z3HuP9QH98+bAKn3L3cDeuVtYe73xyvL8g9s7r4o74uwV+YEyOLMv9dzty93AiS3eh8Kn/vvtuWZz29H/CMit3pvY71/7tvazwmVDCJieGfsV9JuFCsBlS2JiH06oz1mZmZmZmbWBXX+ssmdwgmVtVhEzAUaOrsdZmZmZmZmZl2NRy1lJKlB0kdKjw+RdEbG+IMlLUqTyT4q6WJJbzmHkjaXdH07487L1U4zMzMzMzPrQrqpY291ygmVvBqANxMqETE+Is7NvI8nI6IB2B3YGTis/KSkHhHxfEQcmXm/dU1S3slGzMzMzMzMzFqwTiRUJH1N0rx0Oz2VHStpjqTZkq5MZZtKujGVzZa0X+XoDEmjJI1O9ydJ+rmkaSn20FQ+NJU9nH7uKKkXxbLKI5qXQ5Y0UtJFaZutJU1IbZogaatUPlbShSnOU5LalAiJiEaKJYO3S/u5TtLNwJ3lY5I0XdIupeObJGmvKiG7S/qdpEck3Slp/VS/QdL9qd03StqwFGdIur+xpGfS/V0kPZD6YI6k7VP5Z0rlv62WAGnvtpJel/Q9SdOB/5b0v6VYw1N/mJmZmZmZWUdSB9/q1FqfUEnJgeOAfYB3AydKeg9wJnBQROwBfCVVvxCYnMreBTzShl30iYj9gC8Cl6ayx4BhEbEncDbww4hYmu5fGxENEXFtRZyLgCsiYnfgqtSWZpsB+wMfA9o0okXSBsD7gLmpaF/gcxFxUEXVa4Cj0zabAZtHxINVQm4P/CoidgFeAY5I5VcA30ztngt8p5WmnQz8Io2iGQI8K+mdwAjgPam8CTgmw7Z9gHlpEt4fAe+W1Cc9NwKoPAekfjhJ0kxJM5+6ZXwrh2NmZmZmZmb2VuvCpLT7AzdGxEIASeMovoxfHxHzASLipVT3IODYVNYELGgecdGCq1P9KZL6SxoI9AMuTyMoAmjL2rv7Ap9I968Ezis9d1NELAcelbRpK3G2lTQr7fePEfF/kkYCd5WOs+x/gbsoEiFHA9fViPt0RMxK9x8EBksaAAyMiMmp/PIWtm92H3CmpLcD4yLiCUnvA/YCZkgCWB/4d4Ztm4AboBixI+l24ONp/piPAt+o1sCIGAOMATh64pT6Xm/QzMzMzMyszkUdz3PSkdaFhEq1Mxfp1haNrDxSp3eVWJWPzwEmRsThkgYDk9q4r1pxl5Tut/ZKbJ5DpdLCqjuJeE7Si5J2pxi18QVJWwLNl8NcDNxe0YYmisRFS8r99mafRcQf0iU4HwXukPR5imO6PCK+VQ4g6XBWjHj5fHu2TRanxFiza4EvAS8BMyLitVaOwczMzMzMzGyVrPWX/ABTgMMkbZAu9zicYoTF0ZIGAUjaKNWdAJySyrpL6g+8AGwiaZCk9SguuykbkervDyyIiAXAAOC59PzIUt3XKEavVDMN+GS6fwxw7yoc66q6hmK0xoCImBsR/0iXJTVExMW1NkrH+rKkA1LRZ4Hm0SrPUIwcAXhz3hdJ2wBPRcSFwHiKyXMnAEdK2iTV2UjS1hFxY6kdM9uzbY0mT6K4lOtEalzuY2ZmZmZmZplJHXurU2t9QiUiHgLGAg8A04FLImIq8ANgsqTZwE9T9a8A75U0lyLpsktELKOYTHY6cAvF/ChlL0uaRjGS44RUdh7wI0lTgfLkqhOBnZsnpa2IcxpwnKQ5FImJr7DmXE+RzPnf1ipW8Tng/NTuBoq+ArgAOCX1zcal+iOAeemypJ0o5o15FPg2xYS5cyguQdqsyr5WZ9vmy7huAT6cfpqZmZmZmZl1CEV4ColaJE0CRkXEzM5ui3WM3HOobLVBY85wzF+SdzXo/j2XZ433RlPebPHSzPFy698rb/+9tCRvTrtvj7yf541rwa+Her9c96pP/zZrvM/+4QtZ4+U+xwuW5v3M6tMj73sub7T8/5VavDzvC7pXt7wnuHf3/B8Kg9Zrar1SO/zzjbxXs9f7OVnr/zPayV5blrcH18/8mdWjzn/HrQ1y/57LfU6uOPDAdeYsb/WLyR36l+Pfv1KffbUuzKFitsqWZP4Cf/3NS1qv1A7vHFbrCrJV83pjff9h2CvzX4bLM3+s/2tRfSe4tum3LGu8fy/Oe7wd8Ydhz8yvwfUzf2HMnQC5MnOC5pTrTswaL/cX7g0yx3v81bbMId92uT+zNl0/b1L+jca8DXw985dPgB7Ke45zJ/pzJ6q7ZT7exswJn9xyJzFz/x7ZLPM/wnK/5zriHxu5/3bL/Rqs99eMWSUnVFoQEcM7Y7+SdqNYCahsSVoe2MzMzMzMzKx+dNHklRMqdSgi5lLMV2JmZmZmZmZmdcgJFTMzMzMzMzNbZd266KROXfSwO5ekBkkfKT0+RNIZGeMPlrQorTb0qKSLJbX5XEtaT9KfaqxW1GEknSzp2FbqjJY0Kt0fKWnzNdM6MzMzMzMzsxU8QqVzNABDgNsAImI8MD7zPp6MiAZJPYC7gcOAcc1PSuoREbVm4toT6BkRDZnb1KKIuLidm4wE5gHP52+NmZmZmZmZtYW66BwqXWqEiqSvSZqXbqensmMlzZE0W9KVqWxTSTemstmS9kujPuaVYo2SNDrdnyTp55KmpdhDU/nQVPZw+rmjpF7A94ARzSNA0kiLi9I2W0uakNo0QdJWqXyspAtTnKckHdmWY05Jk2nAdmk/10m6GbhT0kaSbkr7ul/S7pI2AX4PNKT2bVvRh6elUS9zJF2TykZLulLS3ZKekHRiqf7XJc1I9b9bKq/W7+XRJyem7WZLukHSBhXtOJIiKXVVaudHJd1Yev79ksZRhaSTJM2UNPOZW3PnsczMzMzMzKwr6DIjVCTtBRwH7EMxB/F0STOAM4H3RMR8SRul6hcCkyPicEndgb7Ahq3sok9E7CdpGHApsCvwGDAsIholHQz8MCKOkHQ2MCQiTk1tG1mKcxFwRURcLun41JbD0nObAfsDO1GMaLm+Dce9AfA+4GxgU2BfYPeIeEnSL4GHI+IwSQel/TZI+jwwKiI+ViXkGcA7ImKJpIGl8t2BdwN9gIcl3Zr6YHtgKEWfj0/98yLV+71sXET8Lh3D94ETgF82PxkR10s6NbVzpiQBP5H0toj4D8W5vqxan0TEGGAMwKF/uqdD10s3MzMzMzNb13XVESpdJqFCkYi4MSIWAqTRC0OA6yNiPkBEvJTqHgQcm8qagAWSWkuoXJ3qT5HUPyUb+gGXS9oeCKBnG9q5L/CJdP9K4LzSczdFxHLgUUmbthJnW0mz0n7/GBH/lxI3d5WOc3/giNTuuyUNkjSglbhzKEaF3ATcVCr/Y0QsAhZJmkiRRNkf+ADwcKrTlyLBsgfV+71s15RIGZi2u6OlRkVEpJEun5F0GUU/tjgfi5mZmZmZma0+ddGMSldKqFQ7w5FubdHIypdI9a4Sq/LxOcDENNJlMDCpjfuqFXdJ6X5rr9gna8yBsrCVGCsdR0pO7Ak8HxEfAT4KDAMOAc6StEu17dJjAT+KiN9WxDytSv1KY4HDImJ2SgQNb6U+FCNSbgYWA9e1MEeMmZmZmZmZ2WrpSnOoTAEOk7SBpD7A4cCDwNGSBgGULj2ZAJySyrpL6g+8AGySRnGsB1ReDjMi1d8fWBARC4ABwHPp+ZGluq9RjF6pZhrwyXT/GODeVTjWtpqS9oGk4cD8iHi1XCEijouIhoj4iIqVgraMiInAN1gxegTgUEm9U18OB2ZQjCo5XlLftI8t0hwtE6je72X9gH9K6tncxipW6seIeJ5igtpvUyRkzMzMzMzMrINJHXurV11mhEpEPCRpLPBAKrokIqZK+gEwWVITxaUpI4GvAGMknQA0AadExH2SvgdMB56mmB+l7GVJ04D+wPGp7DyKS36+RrHSTrOJwBnpkpwfVcQ5DbhU0teB5rlAOspo4DJJc4A3gM+1Ur878Pt0WZCAn0XEK2l41wPArcBWwDnNyQ1J7wTuS3VeBz4TEY/U6Peysyj6+m/AXKonoMYCF0taBOybLjm6CnhbRDza5l4wMzMzMzMzaydFeE7O1SVpEmly1M5uS2dQsdrR6xFxQR205SKKiXb/py31j79nUtY3wItLuucMx4Cey7PG69U97/s99xC3xU11nH4GenTL23+5j/e/1m/KGu+Nxvo+HwA9Mr8IeyjvOX51Wd4GDuyV9zPhN0f9Lmu8Eb8/OWu83JZn/pOnMXO8HvX/lsuud+bfS29k/lzN/XuuW+ZznPs1nVu9H2/u3+uNy+v/TVzv56Te25fbFQceWP8vmjba/rdTOrS3n/jCsLrsqy4zQsXWfZIepJgj5v91dlvMzMzMzMxs3eaESgYRMbwz9itpN4qVgMqWRMQ+a7IdETF6Te6vlojYq7PbYGZmZmZm1tWoK83OWuKEylosIuYCDZ3dDjMzMzMzM7OupovmkeqLpAZJHyk9PkTSGZli7yZpVrq9JOnpdP9P7YgxMs1NslaQNFDSFzu7HWZmZmZmZl1BV13lxwmV+tAAvJlQiYjxEXFujsARMTcte9wAjAe+nh4fnCN+vZHUnWI5ZydUzMzMzMzMrMN06YSKpK9Jmpdup6eyYyXNkTRb0pWpbFNJN6ay2ZL2kzRY0rxSrFFptRskTZL0c0nTUuyhqXxoKns4/dxRUi/ge8CINHJkRHlEiKStJU1IbZogaatUPlbShSnOU5KObOexf0DSfZIeknSdpL6pfO8Uc7akByQ1L1e8uaTbJT0h6bwaMXdJ28xK7d1+FftptKQrJd2d9ndiKpek81PduZJGpPLhkiZK+gPFEsvnAtumdpzfnn4xMzMzMzOz9ummjr3Vqy47h4qkvYDjgH0AAdMlzQDOBN4TEfMlbZSqXwhMjojD0wiIvsCGreyiT0TsJ2kYcCmwK/AYMCwiGiUdDPwwIo6QdDYwJCJOTW0bWYpzEXBFRFwu6fjUlsPSc5sB+wM7UYw+ub6Nx74x8G3g4IhYKOmbwNcknQtcC4yIiBmS+gOL0mYNwJ7AEuBxSb+MiH9UhD4Z+EVEXJUSRd2BTVehnwB2B94N9AEelnQrsG9qxx7AxsAMSVNS/aHArhHxtKTB6X5DjeM/CTgJYL9RX2PHQz7eShPNzMzMzMzMVtZlEyoUiYgbI2IhgKRxwBDg+oiYDxARL6W6BwHHprImYIGk1hIqV6f6UyT1lzQQ6AdcLml7IICebWjnvsAn0v0rgfLokJsiYjnwqKTWEhdl7wZ2BqaquCCtF3AfsCPwz4iYkdr+KkCqMyEiFqTHjwJbA5UJlfuAMyW9HRgXEU+o9QveqvUTwB8jYhGwSNJEioTJ/sDV6Ry8IGkysDfwKvBARDzdloOPiDHAGIDj75lU56vTm5mZmZmZ1bd6nuekI3XlS36qnfJIt7ZoZOX+610lVuXjc4CJEbEr8PEq27RFOe6S0v32vIQF3NU8t0pE7BwRJ6TyWsdf3lcT0EPS4Vox4e2QiPgDcAjFqJY7JB3EqvVTrfKWjnFhC8+ZmZmZmZmZZdWVEypTgMMkbSCpD3A48CBwtKRBAKVLfiYAp6Sy7ulSmBeATSQNkrQe8LGK+M3ze+wPLEijOwYAz6XnR5bqvkYxeqWaacAn0/1jgHtX4Vgr3Q+8R9J2qY0bSNqB4pKkzSXtncr7Sao5iikibiwlZWZK2gZ4KiIupLgEaXdWrZ8ADpXUO52L4cAMinM2Ip2DtwHDgAeqNK2l/jQzMzMzM7OMvMpPFxMRDwFjKb6QTwcuiYipwA+AyZJmAz9N1b8CvFfSXIqkyy4RsYxiMtnpwC0UyYiylyVNAy4GTkhl5wE/kjSVYn6RZhOBndNIjxEVcU4DjpM0B/hsastqiYj/UCR0rk5x7wd2ioilFAmOX6bjv4v2jaIZAcyTNItiXpcrVrGfoDgvt6a2nRMRzwM3AnOA2cDdwDci4l9Vju9FisuZ5nlSWjMzMzMzM+sIivAUErlJmgSMioiZnd2Welarn9IqQK9HxAUd3YavTb876xugZ7e876eXl3RvvVI79O6et32Lm/Kmi3PHy23rvsuyxrvnX+tnjbfzhkuzxnt1Wd6c+8LM8QDW77E8a7yFjXnb2CPzS3rT9Ruzxnsj8/Fe+5mLs8Y75boTs8Z7ozHvCfnP4q41FV3uz0CApcvznpOXluR9TS+PvO3rpry/h3P3X+6/E3J/6uf9xIfnFuZ9Dw/q3ZQ1Xu7zAfX/3/Tcr+kemf82z92+/33vsPr+47cddh17T4cmFuaNPKAu+6re31NmZmZmZmZmZnWna/1rZQ2JiOGdsV9Ju1GsBFS2JCL26Yz2tKZWP0XE6DXbEjMzMzMzM1tV6qJDNZxQWYdExFygobPbYWZmZmZmZrau66J5pIKk11t5fqCkL5Yeby7p+sxtmCRpSJXyIZIuXMWYu0i6W9JfJD0h6SypmBtZ0nBJ+5XqjpV05KofwaqR9IykjVup898Vj6d1bKvMzMzMzMysvbzKzzpKhVU9zoHAmwmViHg+ItZI8iEiZkbEae3dTtL6FEsWnxsROwB7APux4jiGp8errbW+lbS6M6qulFCJiCztNjMzMzMzs3ycUFmHSBos6c+Sfg08BJwlaYakOZK+W6V+X0kTJD0kaa6kQ9NT5wLbpuWMz09x56Vteku6LNV/WNJ7U/lISeMk3Z5Gh5yXyrun0SDz0jZfLTXhKEkPpBElB6T6wyXdku6PlnRlGnXyhKSWlj34NDA1Iu4EiIg3gFOBMyQNBk4GvpqO6YC0zTBJ0yQ9VR6tIunrlf1WpW+3rOjL1yV9T9J0YF9Jn0nHNkvSb6slWSTdJOlBSY9IOimVnQusn7a7qjl2+nmtpI+Uth8r6YjUx+eX2vyFFvrJzMzMzMzMbJWtkwmVZEfgCuCbwBbAUIr5RfaSNKyi7mLg8Ih4F/Be4CfpEpkzgCcjoiEivl6xzZcAImI34FPA5ZJ6p+cagBHAbsAISVumsi0iYte0zWWlWD0iYihwOvCdGsezO/BRYF/gbEmb16i3C/BguSAingT6Ai8BFwM/S8d0T6qyGbA/8DGKJBKSPgBsT/V+2xG4IiL2jIi/Vey/DzAvTYT7YuqH90REA9AEHFOlzcdHxF7AEOA0SYMi4gxgUWpn5TbXpLhI6gW8D7gNOAFYEBF7A3sDJ0p6R+XOJJ0kaaakmXNuuqVKc8zMzMzMzKyt6mGEiqQPSXpc0l8lnVHl+WPSP97npAEFe6zuca/Lk9L+LSLul3QB8AHg4VTelyJRMKVUV8APU8JgOUUCZtNW4u8P/BIgIh6T9Ddgh/TchIhYACDpUWBr4BFgG0m/BG4F7izFGpd+PggMrrG/P0bEImCRpIkUiY6bqtQTUGsN8FrlN0XEcuBRSc3H/QGq99vfSX1bI1YTcEO6/z5gL2BGkZ9ifeDfVbY5TdLh6f6WaT8v1ogP8H/AhZLWAz4ETImIRSkJtHtplM2AFOvp8sYRMQYYA/C16Xd36HrpZmZmZmZm1rHSlRC/At4PPEvxHXR8RDxaqvY0cGBEvCzpwxTfCVdrRdx1OaGyMP0U8KOI+G0LdY8B3gbsFRHLJD0D9G6hfnPcWpaU7jdRjEB5OWXAPkgxuuVo4PiK+k3UPieVX/xrJQIeAVYagSNpG+D1iHhN1dN75faq9PMt/ZYuG1qY7ndnxWiY8RFxNrA4IppKMS6PiG/VaCuShgMHA/tGxBuSJtFK30fE4lTvgxQjVa4u7e/LEXFHS9ubmZmZmZlZPt06f56TocBfI+IpAEnXAIcCbyZUIqK8yMn9wNtXd6fr8iU/ze4AjpfUF0DSFpI2qagzAPh3Sqa8l2JECcBrQL8acaeQLl+RtAOwFfB4rUaoWNGmW0TcAJwFvKudx3FomrdlEMXEsjNq1LsK2F/SwWm/6wMXAuel51s6prJW+y0imtIlOQ0pmVJpAnBk83aSNpK0dUWdAcDLKZmyE/Du0nPLJPWs0b5rgOOAA1Jbm9t8SvM2knaQ1KcNx2pmZmZmZmZ1qjxtQ7qdVFFlC+AfpcfPprJaTqC48mG1rMsjVACIiDslvRO4L43OeB34DCtfenIVcLOkmcAs4LG07YuSpqqYiPb/KIYQNfs1cLGkuUAjMDIiltQYAQLFybxMK1bFqTlqo4YHKC4V2go4JyKer3G8i1RMqvtLSb8CugNXAhelKjcD16c6X661sxb6ranWNlViPCrp28Cd6biXUYzOKc+7cjtwsqQ5FAmp8qVEY4A5kh6qMo/KnRRz5IyPiKWp7BKKS6YeSnPg/Ac4rK3tNTMzMzMzs/br6JV4ytM21GpCtc2qViwGUZxAMY3HalGEp5Cod5JGU1yyc0Fnt2Vd88jLt2R9A1zx1/VzhmPZ8ryfTPttsrT1Su0w7d+9ssb716LVXWl7Zb0yj8E7defXs8Y7+jtLWq/UDiOOG5A13nML8+fcn3sjb8wt+zRmjffUgrztG9w/b/s26d3mnHabzF+S9z230Xp52/ebo36XNd5pN3w+a7x/ZH6PNEb+v0Y36J7377we3fLGe1vm1/QLmX+PLM98TpbX+Z/ddTBkv0W5+69X5vfH0qb8Hbg8c7yucHnC2uSKAw+s83dd2+119T0d+gn34KcOaLGvJO0LjI6ID6bH3wKIiB9V1NsduBH4cET8ZXXbtc6PUDEzs/qQO5liZi2r92SKmbUsdzLFrCN19AiVNpgBbJ9WeX0O+CTw6XIFSVtRLAjz2RzJFHBCZa0QEaMryyTtRnEpT9mStFyxmZmZmZmZWZcQEY2STqWYV7M7cGlEPCLp5PT8xcDZwCDg12lai8aIGLI6+3VCZS0VEXOBhs5uh5mZmZmZmXVtqoNrBiPiNuC2irKLS/c/D2S9/teX0WUiqcXJFSQNlPTF0uPNJV2fuQ2TJL0lwyZpiKQLVzHmLpLulvQXSU9IOkstzLybk6Rn0upIleWHSDoj3R8taVSVOoPTZMJmZmZmZmZm2Tmh0g4qrGqfDQTeTKhExPMRcWSWhrUiImZGxGnt3S4tuTweODcidgD2APajdBydISLGR8S5ndkGMzMzMzMzK0gde6tXTqi0Io10+LOkXwMPAWdJmiFpjqTvVqnfV9IESQ9JmpuWJwY4F9hW0ixJ55dHUEjqLemyVP/htIwTkkZKGifp9jQ65LxU3l3SWEnz0jZfLTXhKEkPpBElB6T6wyXdku6PlnRlGnXyhKQTWzj8TwNTI+JOgIh4AzgVaB4dMjeNvJGkFyUdm8qvlHRwrfZX6bPuki5I8eZIKi/n/OVSX+5U6peLqsTZS9JsSfdRLM9sZmZmZmZm1iGcUGmbHYErgG8CWwBDKeYv2UvSsIq6i4HDI+JdwHuBn6RLZM4AnoyIhoj4esU2XwKIiN2ATwGXS+qdnmsARgC7ASMkbZnKtoiIXdM2l5Vi9YiIocDpwHdqHM/uwEeBfYGzJW1eo94uwIPlgoh4EugrqT8wFXhPqvcUcECq9m7g/hbaX+kk4B3AnhGxO3BV6bn5qS9/A7zl0p4KlwGnRcS+LVWSdJKkmZJmXjf29lZCmpmZmZmZWUs8QsVa8reIuB/4QLo9TDFaZSdg+4q6An4oaQ7wJ4oEzKatxN+ftGJPRDwG/A3YIT03ISIWRMRi4FFga4rkxTaSfinpQ8CrpVjj0s8HgcE19vfHiFgUEfOBiRQJomoE1FojMYB7gGHp9htgN0lbAC9FRPOcMtXaX+lg4OKIaEx98FI7jwdJA4CBETE5FVWugLSi4RFjImJIRAw5auSHalUzMzMzMzMzq8kJlbZZmH4K+FEaZdIQEdtFxP9U1D0GeBuwV0Q0AC8AvWlZSzm3JaX7TRQjUF6mmM9kEsXolkuq1G+i9ipOlUmSWkmTR4CVJrmVtA3wekS8BkyhGJVyQGrLf4AjKRItNdsv6fB06dOsNIluS4mbthwPrcQwMzMzMzOzDuIRKtYWdwDHS+oLIGkLSZtU1BkA/DsilqW5UJpHZLwG9KsRdwpFIgZJOwBbAY/XakRa+aZbRNwAnAW8q53HcWiat2UQMByYUaPeVcD+kg5O+10fuBA4DyAi/gFsDGwfEU8B91JclnNP9XCFiLixlJSaCdwJnCypR9rPRu08HiLiFWCBpP1T0THtjWFmZmZmZmbWVk6otEOanPUPwH2S5gLX89YkyVXAEEkzKb7UP5a2fRGYmiaSPb9im18D3VPMa4GREbGE2rYAJkmaBYwFvtXOQ3kAuJVinpNzIuL5apUiYhFwKPBtSY8DcymSL+UJYacDf0n370ltu7ed7bkE+DswR9JsislwV8VxwK/SpLSLVjGGmZmZmZmZtUM3deytXrV0CYUBEfEMsGvp8S+AX1Sp1zf9nE8x2Wu1WJWJgl1T+WJgZJX6YykSJs2PP1Z6+i2jUiJieOn+fNKcIxExieKSnGZ/iYiTqrWxSsy5FKNYaj3/2dL9aZSSdK20vxyjEfhaupXLB5fuz2xuRzluRIwu1XmQ4lKoZqMxMzMzMzMz6wBOqFiX9t1Z/bPGW17ns7g0/itveveVpfU9yO3FJXnbd9xteV8vJ520OGu8WS/1zBqvf8/lWeNt339p1ngAi5vynuO392vMGi9vD8Ljr+Y9xxv2ytvCNxrzfsacdsPns8a78IhLWq/UDodfcXLWeL275f0l0pj7d9Ly/P8ifP/mLQ3Ibb9L/tIna7wemQ859znJ3b7csr8GM2vM/JrO/ZnfEX9l1fs5yT0Sob7/Ul231PM8Jx3JCZUupjyio5mk3XjrqjhLImKfNdIoMzMzMzMzW2upi2avnFCx5st6Gjq7HWZmZmZmZmZrCydUzMzMzMzMzGyVddVLfrrowJy1n6TXW3l+oKQvlh5vLun6zG2YJGlIlfIhki5chXg3Sjqs9PhxSd8uPb5B0icknSzp2FS2k6RZkh6WtO0qHoqZmZmZmZlZu3iESh2TJEARsSpzXA0EvkixJDNpaeQj87WutrQiz8xV2HQasB9wk6RBwOusvGLSvsCXIuJfpbLDgD9GxHdWsblmZmZmZma2GtRFh6h4hEqdkTRY0p8l/Rp4CDhL0gxJcyR9t0r9vpImSHpI0lxJh6anzgW2TaM3zk9x56Vteku6LNV/WNJ7U/lISeMk3S7pCUnnpfLuksZKmpe2+WqpCUdJekDSXyQdkOoPl3RLuj9a0pWS7k4xT2zh8KdSJFRIP28B3qbCO4BFEfGvFHOUpI8ApwOflzQx7e8zqT2zJP1WUvcqfXaSpJmSZj51y/i2nBYzMzMzMzOzlXiESn3aETgOuIliVMlQQMB4ScMiYkqp7mLg8Ih4VdLGwP2SxgNnALtGRAMUiZrSNl8CiIjdJO0E3Clph/RcA7AnsAR4XNIvgU2ALSJi1xRrYClWj4gYmpIb3wEOrnI8uwPvBvoAD0u6NY2YqfQgsKukXhQJlcnANsA7U5umlitHxG2SLgZej4gLJL0TGAG8JyKWpaTUMcAVFduNAcYAHD1xSp0vHmdmZmZmZlbfuugAFY9QqVN/i4j7gQ+k28MUo1V2AravqCvgh5LmAH8CtgA2bSX+/qRlkiPiMeBvQHNCZUJELIiIxcCjwNbAU8A2kn4p6UPAq6VY49LPB4HBNfb3x4hYFBHzgYkUCaK3iIglwCPAuygSMNOB+yiSK/tRXBLUkvcBewEzJM1Kj7dpZRszMzMzMzOzdvMIlfq0MP0U8KOI+G0LdY8B3gbslUZlPAP0biV+S/nDJaX7TRQjUF6WtAfwQYrRLUcDx1fUb6L266lyFEhLo0Kmwf9n7+7jrSrr/P+/3twJCIpKmTUkhaLj7VEhTVMwyRlKiNIiZUzUgdHpvlHzZ95l820YM/1OpaNoiTgMIqkF1iCogQaCIHKTQvo1cawsTRABb+DA5/fHuo4sN2efs885a3O2nveTx36wbq79Wde19tr7nP0513UtTgB6p+MuBL5M1kPlxiaeB1m7bouI/6+ZcmZmZmZmZlYQ91CxWnQfcI6kXgCSPiDpvSVldgdeTMmUE8l6lABsAHqXifsQWSKGNNTng8DvylUiDSXqFBF3AZeR9SBpiU+neVv2AoYCi5soOx/4J2B5Wl9B1lvlg2S9V5ryAHBawzmStKekfZt5jpmZmZmZmVmLuYdKDYuI2WlekEfSrMkbgX8AXswVmwLMlLQEWAasTs99WdL8NBHt/wDX555zA3CjpJVAPTA2It5sYmbmDwC3SmpIwLW0B8ijwC/JkiLfLTN/SoMFZMN0/i21o17Si8Dzzd3tKCKeTLdZnp3quoWsR81zLayvmZmZmZmZVaij9lBRhOfktOqRdCVp0tj2rktjxj40r9A3wMtvFtvpa49urbljdnmdavyDblvBH0fFnr3aV3SXwy6dav/nw7Yo9qLeXPBF06Xg99ym+mJf5V06F9vgN7fWdv2Kds8XmxuJ2jLjpo0vNN7G+mIvwH85ZGOh8QC+v7JcZ9rWmb+q2DYf97e1/zlYpPqCm1v0Z+D/W7fDzRvb5MC96guNt3lrsQ2uxu9tRf+uVbSFTxXb6Pe9r9hrZu3Lxf5c+u3Y42v8t/PKnfir+VW9un79yeNq8ly5h4qZmZmZmZmZtVqt/+G2WpxQsaqKiCtLt0k6lHSXoZw3I+LonVIpMzMzMzMzszZyQsV2uohYCdS1dz3MzMzMzMys7TpqDxXf5acdSfqGpDck7V5B2QUFHnerpGWSfitpuqSebYw3VNK9TezvnybHbbPmjmVmZmZmZma2MzihUoYy1T4/p5PdQvgzzRWMiGMLPO7rEVEXEYcAm4HzCoxdUyS5F5aZmZmZmVkVdVJU9VGrnFDJST0pVkm6AVgK/ETSEklPSPpOrtxgSQskLZf0qKTekjpL+r6kxZJWSPqnZo41AOgFXEqWWGnYfnCKuSzF2T9t35j+7yXpAUlLJa2U9OmSut+c6jtbUo8Kmv0wsJ+kEZIWSXpc0v2S9s4d79Z0rBWSTm2mXUNS3ZelWL1L9veX9HCq/1JJx6btQyXNlfQzSaslTVG6j7Okv0/bfgN8NhdrV0k/Tef88dy5GJt63swEZldwDszMzMzMzMxaxAmVHR0ATI6II4B/iYhBwGHAEEmHSeoGTAO+FhGHA8OA14FzgfURMRgYDIyT9KEmjnM6MJUsoXGApPem7ecB/xERdcAg4A8lz3sD+ExEHAmcCPygIfEA7A9cHxEHA68AzSU/ugDDgZXAb4BjUrvvAC5KxS5L7To0Ig4DHmwqJnAB8KVU/+PJzk3ei8AnUv1HAz/M7TsC+DpwEPBh4DhJ3YGbgREp3vty5b8NPJjO+YnA9yXtmvZ9FDgrIj7eSLvHp0TZkqdmzGymOWZmZmZmZtaUTqruo1Z5OMSOnouIhWn585LGk52nfci+6AfwQkQsBoiIVwEknQwcJum09NzdyRIcz5Y5zhfIEiPbJN0NfA64HngE+LakvwHujoinS54n4HuSTgC2AR8A9k77no2IZWn5MaB/mWP3kNRQ7mHgJ2SJpGmS9gG65eo9LNWV1N51ZWI2mA9cK2lKqv8ftud7AOgK/FhSHbAVGJjb92hE/AEg1a8/sDG16+m0/b+A8an8ycBISRek9e7AB9PynIhY21gFI2IiMBFg7EPzarf/mJmZmZmZmdUsJ1R2tAkg9S65ABgcEeskTSL7wi6ypEopAV+JiPuaO4Ckw8iSLXNSsqEb8Huy3iX/LWkR8CngPkn/GBH5XiFjgPcAR0XEFklrUr0A3syV20qWOOkHNHTDuDEibiTNoVJSpx8B10bEDElDgStz7YqSsp8Brkir/5jfFxETJP0S+CSwUNIwsl41Db4B/AU4nKyHVH5faf0brs9ySQ8Bp0bE70rqdzTpdTQzMzMzM7Pq6qhDXzpquyuxG9mX8vVpPpHhaftq4P2SBgOk+VO6APcB50vqmrYPzA0/KXU6cGVE9E+P9wMfkLSvpA8Dv4+IHwIzyIYb5e0OvJiSKScC+zbViIh4Pk1AW5eSKeXsDvwxLZ+V2z4b+HLDiqQ9IuKeXMwl+SCSBkTEyoj4d2AJcGAjx3khIrYBZwKdm6o/2fn+UJpzBnLzzZCd86/k5lo5oplYZmZmZmZmVjBPSmtvExHLgceBJ4Cfkg1lISI2k8398SNJy4E5ZD1EbgGeBJYqu0XwTZTvAfQF4J6Sbfek7aOB36YhLwcCk0vKTQEGSVpC1ltldetb+TZXAtMlPQz8Nbf9X4E9lN1ieTnZXCVN+Xqu7OvA/5TsvwE4S9JCsuE+TfYkiYg3yIb4/DJNSvtcbvd3yYYQrUjn/LvN1M3MzMzMzMysEIqo3WyPWbUVPYfKnrtsLTIcG7cUm/Ps1rnY93vRGdlXNtd2jvdDvbcUGm/JX7s3X6gFiq5f/bZCw7F2c3Md0lpu67ZiZynbWF9svN27FXsS+9b4Z8ym+mLjde5U7GdW94Lj9Sn49b159MRC4535303ecLDFJn7s/YXGA7hi6f8WGu/5TbU9mv21gt8jf36t2M/V/r3rC41X9F+Vt0Wxn9E9uxT7Hi769a2Gol+T+oJ/Dncp+HO61uv30+OH1vB0qy3z6fsfrmpi4RfDjq/Jc1X773ozMzMzMzMzsxpT22n8dzhJhwK3l2x+MyKObo/6mJmZmZmZmRWto/bUcEKliiJiJVDX3vUwMzMzMzMzs2J11ESStTNJ/SW9LmmZpOWSFkg6IO0bKuneVsZdI6lvsbU1MzMzMzOzcjqpuo9a5YSKlaVMNa+RZ9Ktlw8HbgMuqeKxzMzMzMzMzArjhIq9Teo5skrSDcBS4CeSlkh6QtJ3cuUGp14lyyU9Kqm3pM6Svi9psaQVklpye4HdgHWN1Ocj6TiPl/Ri6SzpGkkr07G+UvK8HpJmSRrXujNhZmZmZmZmlZCiqo9a5TlUrDEHAGdHxD9L2jMi1krqDDwg6TBgNTANGB0RiyXtBrwOnAusj4jBknYB5kuaHRHPljnOAEnLgN5AT6CxyXpXAydERL2kYcD3gFOB8cCHgCPSvj1zz+kF3AFMjojJpQEljU/P56P/8i8MHDmiRSfHzMzMzMzMzAkVa8xzEbEwLX8+JSC6APsABwEBvBARiwEi4lUASScDh0k6LT13d2B/oFxC5ZmIqEvPHQ1MBP6+pMzuwG2S9k/H7Zq2DwNujIj6VIe1uef8Arg6IqY0dtCImJiOxdiH5tVuutPMzMzMzOwdoJbnOakmD/mxxmwCkPQh4ALgpIg4DPgl0B0QWXKjlICvpHlR6iLiQxExu8JjzgBOaGT7d4FfR8QhwIh0/IZjlUuGzAeGS+qgb2szMzMzMzOrNidUrCm7kSVX1kvaGxietq8G3i9pMECaP6ULcB9wvqSuaftASbtWeKyPAc80sn134I9peWxu+2zgvHRcSob8XA68DNxQ4bHNzMzMzMyslTpV+VGrarlu1s4iYjnwOPAE8FOynh9ExGZgNPAjScuBOWQ9R24BngSWSvotcBNNDysb0HDbZLK5Uf6xkTJXA/8maT7QObf9FuB/gRXp+WeUPO/rQHdJV1feYjMzMzMzM7PKKMJTSFjHNf43cwt9A3TrXOz7aeOW2s55dip4xu36bbU9SqvosaH1BX/8dutUbMDNBb8e1biaC69jwdd00ddMl4LjbSv4GtxUX+yr3LvrtkLj1fp7rujX9/Yzbio2IHDD/WMLjTfvz92bL9QCRV/T6zYXe00X/XO93671hcbraJ7fVOx0knt131povK1V+L1o14I/V2v7N9XaN3nIkNr+5bcFzphb3bkp/3to8+dK0t8D/0H2h/hbImJCyX6l/Z8EXgPGRsTSttTL7wEzMzMza1bRyRQzM7OipLvSXk82TcVBwOmSDiopNpzspin7k9319T/belzf5ceqStKhwO0lm9+MiMZukWxmZmZmZmbvMDVwl5+PAP8vIn4PIOkO4NNkU1I0+DQwObJhOgsl9ZG0T0S80NqDOqFiVRURK4G69q6HmZmZmZmZvWt9AHg+t/4HoPSP+I2V+QDQ6oSKh/xYu5B0sKQHJT0l6WlJlzXc5ljSUEnH5spOknRa+9XWzMzMzMzMyqn2XX4kjZe0JPcYX1KFxvrIlM7rUkmZFnEPFWtUSm4oIoqduSqL3QOYAZwfEbMl9QTuAv6ZbNzbUGAjsKCAY1WtHWZmZmZmZlZ9ETERmNhEkT8A/XLrfwP8qRVlWsQ9VOwtkvpLWiXpBmAp8JOU/XtC0ndy5QZLWiBpuaRHJfWW1FnS9yUtlrRC0j81cagzgPkRMRsgIl4DvgxcLKk/cB7wjXRL5ePTc05Ix/x9vreKpAtzx/xOmXbk3zRmZmZmZmZWoE6q7qMCi4H9JX1IUjfgC2R/xM+bAXxRmWOA9W2ZPwWcULEdHUA2Uc8RwL9ExCDgMGCIpMPSxTkN+FpEHA4MA14HziW7IAcDg4Fxkj5U5hgHA4/lN0TEM0AvYC1wI3BdRNRFxMOpyD7Ax4BTgAkAkk4mm6H5I2TztBwl6YTSdkTEc/lj5buLrZoxsxWnyMzMzMzMzGpFRNST/ZH+PmAVcGdEPCHpPEnnpWK/An4P/D/gZrIREm3iIT9W6rmIWJiWP5/GpnUhS2gcRDbG7IWIWAwQEa/CW8mNw3K9R3YnS3Y828gxRPmxauW2/zwN23lS0t5p28np8Xha75WO+b8l7Xj7AXLdxcb/Zm5V75duZmZmZmb2btdJ7f+1KiJ+RZY0yW+7MbccwJeKPKYTKlZqE0DqXXIBMDgi1kmaBHSnfDJEwFci4r4KjvEEcEJ+g6QPAxsjYkOam7bUmyXHavj/3yLippJY/RvaYWZmZmZmZtVVA7dNbhce8mPl7EaWlFifeoQMT9tXA++XNBggzZ/Shaxr1fmSuqbtAyXtWib2FOBjkoalsj2AHwJXp/0bgN4V1PE+4BxJvVKcD0h6bwvbaWZmZmZmZtZi7qFijYqI5ZIeJ+tN8ntgftq+WdJo4EcpEfI62TwqtwD9gaXpzjovAaPKxH5d0qdTjOuBzsDtwI9TkZnAz1KZrzRRx9mS/hZ4JPVq2Qj8A7C1DU03MzMzMzOzFuioPTWcULG3RMQa4JDc+tgy5RYDxzSy65L0qORYK8luj9zYvqfIJsJt8HDJ/l655f8A/qORMIc0ss3MzMzMzMysEE6oWIf2ypZic6lb3ih28GDXzsVO7rRla7H121ZoNOjdtdiIRWfK12wo9iOzbq83my/UAs9u7FpovK6dir3+div49QXo2aXYmL26FNvmoj9jNhYcr1vBr/G+vbYUGu/VgtvLtmI/A//lkI2Fxjt4j4GFxvvo/WMLjffPwyYVGg/gmBu/XGi8E973eqHx2FDs52q/XesLjVf0z80/vlbsz7n6gt9z7+1ebCfkzduKjVf0ZyoF/x4IUF90yILnzRjQu9ifI+ve7FxovF2r8LvMu0UtTErbHpxQsaqRdCjZUJ68NyPi6Paoj5mZmZmZmVlRnFCxqknDeuraux5mZmZmZmZWPb7Lj5mZmZmZmZmZVcQJFdupJN0jaVRu/XeSLs2t3yXps5LGSvpxmRi/ktQnPf55J1TbzMzMzMzMyuik6j5qlRMqtgNlqnVtLACOTcfZi+xWxx/N7f9oKlNWRHwyIl4B+gBOqJiZmZmZmdlO54SKASCpv6RVkm4AlgI/kbRE0hOSvpMrN1jSAknLJT0qqbekzpK+L2mxpBWS/qmJQ80nJVTS//cC70lJnA8Br0fEn9P+90uaJelpSVfn6rBGUl9gAjBA0jJJ30/7LszV4zs0QtL41LYlv793RivPmJmZmZmZmUGWWKjmo1Z5UlrLOwA4OyL+WdKeEbFWUmfgAUmHAauBacDoiFgsaTfgdeBcYH1EDJa0CzBf0uyIeLaRYzwGHCKpG1lCZR7wYeBvgSPIEi4N6tK2N4HfSfpRRDyf238xcEhE1AFIOhnYH/gI2U3cZkg6ISIeylcgIiYCEwE+/+uHOub9vczMzMzMzKxNnFCxvOciYmFa/ryk8WTXyD7AQUAAL0TEYoCIeBXeSmQcJum09NzdyRIbOyRUIuJNSU8ARwLHAFeTJVSOJUue5If7PBAR69MxngT2BZ6nvJPT4/G03ivV46GyzzAzMzMzM7M26aSO+XdqJ1QsbxNAGnpzATA4ItZJmgR0J+v10dg7RcBXIuK+Co+zADgB6J3iLwS+TJZQuTFX7s3c8laav14F/FtE3FRhPczMzMzMzMxapZaHI1n72Y0subJe0t7A8LR9Ndm8JoMB0vwpXYD7gPMldU3bB0ratYn484F/Apan9RVkvVU+CDzRgnpuAHrn1u8DzpHUK9XjA5Le24J4ZmZmZmZm1kId9S4/7qFiO4iI5ZIeJ0tu/J40r0lEbJY0GviRpB5k86cMA24B+gNLJQl4CRjVxCEWkA3z+bcUt17Si8DzEbGtBfV8WdJ8Sb8F/iciLpT0t8AjWTXYCPwD8GLFjTczMzMzMzOrgCI65lgnM4Dhs39T6BvgPbtsLTIc6zYX24nsuT83X6Yl9tyz2Pp9sFd9ofG6dCr28+3X924sNN7Xv9it0HjzX+xeaLyXXu9caLxq/HVhj4Lfc8e9983mC7XAn18v9j2yvuDPhG1R7IvSrXOx77neXSvOsVfkE+8v9vWd/P+a6ozZcvv22lJovOc2di04XvF/h1t43o8LjfeZyecVGq9bwX25i55joOj38LaCvxbUFxyvS8E/R17bWmzAXl2Kfn0LDZfFLDhe0cMdarknAhT/mkweMqTGW1y5CxY9WNXEwjVHf7wmz5WH/JiZmZmZmZmZtZCH/FhVSDoUuL1k85sRcXR71MfMzMzMzMyqo9Z7F1WLEypWFRGxEqhr73qYmZmZmZmZVYOH/FirSZok6bQC4lwpKSTtl9v2jbRtUDPPndtcGTMzMzMzM6seKar6qFVOqNhOk26xXM5K4Au59dOAJ6tbIzMzMzMzM2urjnrbZCdUOghJl0laLWmOpKmSLpA0QNIsSY9JeljSgansJEk/lLRA0u8beqEo82NJT0r6JfDeXPyjJM1Lse6TtE/aPlfS9yTNA77WRBV/Dnw6PefDwHqy2y83xP9PSUskPSHpO2XaeLKkRyQtlTRdUq8y5canWEue/9WMFpxFMzMzMzMzs4wTKh1AGhJzKnAE8FmgYYjMROArEXEUcAFwQ+5p+wAfA04BJqRtnwEOAA4FxgHHpvhdgR8Bp6VYPwX+Ty5Wn4gYEhE/aKKarwLPSzoEOB2YVrL/2xExCDgMGCLpsJI29gUuBYZFxJHAEuCbjR0oIiZGxKCIGNTvkyObqJKZmZmZmZk1p1OVH7XKk9J2DB8DfhERrwNImgl0J0uITJfe6kO1S+45P4+IbcCTkvZO204ApkbEVuBPkh5M2w8ADgHmpFidgRdysUqTI+XcQTbs5++Ak4Czc/s+L2k82TW7D3AQsCK3/5i0bX6qQzfgkQqPa2ZmZmZmZtYiTqh0DI2NOusEvBIRdWWe82aZ5zc2I5CAJyLio2VibWq2hpmZwPeBJRHxakOiR9KHyHrQDI6IdZImkSWESuswJyJOr/BYZmZmZmZmVoBONTxxbDXVcu8ZK85vgBGSuqd5RT4FvAY8K+lz8Nb8KIc3E+ch4AuSOqc5Uk5M238HvEfSR1OsrpIObmklUw+ab/H24UIAu5ElZdan3jLDG3n6QuC4hjsFSeopaWBL62BmZmZmZmZWCfdQ6QAiYrGkGcBy4Dmy+UXWA2OA/5R0KdCVbMjN8iZC3QN8nOyOPE8B81L8zWni2h9K2p3suvq/wBOtqOsdjWxbLunxFO/3wPxGyrwkaSwwVVLD0KVLUz3NzMzMzMysSmr5TjzVpIiO2TWno5HUKyI2SupJ1tNkfEQsbe96tbcz5s4r9A2wLYr9JOnSqdj3Z9Fd0or+4NxW4x9Hb2wrtsG7dd1WaLzNW4ut3zvhB2N9wddMfcGvcbeC38OvFfwad+9cbP2Kfg8XXb83Cj5/i39XbLzjDyr2M6Ho6/lDvbcUGg/g6Ve7Fhrvni/eWGi8z91+XqHxrLZ0tN9jrPZMHjLkHfDbVmWuWHp/Vd8B3zlyWE2eK/dQ6TgmSjqIbO6R25xMMTMzMzMzsyK8E/4QVw1OqHQQEXFGe9dB0reBz5Vsnh4RpXOmmJmZmZmZmdU0J1Rsp0mJEydPzMzMzMzM3kU6t3cF2onv8rMTSRqVht00rF8laVh71qlSki4pMNb7JN0h6RlJT0r6laSBkoZKurfMc27JnzszMzMzMzOz9uQeKjvXKOBe4EmAiLi8XWvTMpcA3yvdKElkkxtXNJNeKn8P2TwuX0jb6oC9m3peRPxjSytsZmZmZmZm1ddJHXNWZvdQaQNJ/SWtknSzpCckzZbUQ9I4SYslLZd0l6Seko4FRgLfl7RM0gBJkySdJmm4pDtzcYdKmpmWT5b0iKSlkqZL6lWmLhdJWpmOOSFtq5O0UNIKSfdI2iNtnytpUFruK2lNWh4r6W5JsyQ9LenqtH0C0CPVe0qu3TcAS4HLJF2Xq8s4SdeWOW0nAlsi4q1p+CNiWUQ8nFZ7SfqZpNXpWGqkzhsl/Z/U1oWS9k7bR0haJOlxSfc3bDczMzMzMzMrmhMqbbc/cH1EHAy8ApwK3B0RgyPicGAVcG5ELABmABdGRF1EPJOLMQc4RtKuaX00ME1SX+BSYFhEHAksAb5ZWgFJw8l6vxydjnl12jUZ+FZEHAasBK6ooD116fiHAqMl9YuIi4HXU73HpHIHAJMj4gjgGmCkpIZ7H54N3Fom/iHAY00c/wjg68BBwIeB4xopsyuwMLX1IWBc2v4b4JhUpzuAixo7gKTxkpZIWvL/Zs5soipmZmZmZmbWnE6q7qNWOaHSds9GxLK0/BjQHzhE0sOSVgJjgIObChAR9cAsYISkLsCngF8Ax5AlFuZLWgacBezbSIhhwK0R8VqKt1bS7kCfiJiXytwGnFBBex6IiPUR8QbZ0KTGjgfwXEQsTMfbBDwInCLpQKBrRKys4FiNeTQi/pCGEC0jO5+lNpMNnYLt5xzgb4D70nm/kDLnPSImRsSgiBi034gRraymmZmZmZmZdWSeQ6Xt3swtbwV6AJOAURGxXNJYYGgFcaYBXwLWAosjYkMa7jInIk7PF5R0NHBTWr0cENCSQWv1bE+mdS/ZV9qectfIppL1W8jmWVlN+d4pAE8ApzWxv5Ljb4mIaKTMj4BrI2KGpKHAlU0cx8zMzMzMzApQy71Iqsk9VKqjN/BCGgIzJrd9Q9rXmLnAkWTDV6albQuB4yTtB5DmYhkYEYvS8Ju6iJgBzAbOkdQzldszItYD6yQdn2KdCTT0VlkDHJWWm0pu5G3JDenZQUQsAvoBZwBTm4jzILCLpIZhOkgaLGlIhfVoyu7AH9PyWQXEMzMzMzMzM2uUEyrVcRmwiGxulNW57XcAF6ZJUwfknxARW8mGsQxP/xMRLwFjgamSVpAlWA4sPVhEzCKbn2VJGhp0Qdp1FtkkuCvI5ka5Km2/Bjhf0gKgb4VtmgiskDSliTJ3AvMjYl25AqlnyWeAT6TbJj9B1pPkTxXWoylXAtMlPQz8tYB4ZmZmZmZm1ozOqu6jVmn7yAmztpF0L3BdRDzQ3nWp1D/Mm1foG6B752LfT/Xbiv302Fbw272ie2W3gDO8bVN0V8uir5dqeKPg90i3TsU2enPB9etZ8GdM0a9x0e0t+jO16PYW/Z4r+paTL73RudB4e3Qr+lO/9j+3pp95Y/OFWuBzt59XaDxrm1q//uzdb/KQITWcKmiZH6ycU9V3wL8c+omaPFeeQ8XaTFIf4FFg+TspmWJmZmZmZmZt11HnUHFCxdosIl4BBua3SdoLaCy5clJEvLwz6mVmZmZmZmbVV3Qvy3cKJ1SsKlLSpK6962FmZmZmZmZWDZ6yoAKSRkk6KLd+laRh7VmnSkm6pMBY75N0R5pM9klJv5I0sIny/SX9tqBjj5X04zL7FhRxDDMzMzMzM2u5Tqruo1Y5oVKZUcBbCZWIuDwi7m+/6rRIowkVZSp+/SUJuAeYGxEDIuKgFHvvYqrZehFxbHvXwczMzMzMzDqWDplQST0nVkm6WdITkmZL6iFpnKTFkpZLuktST0nHAiPJbj+8TNIASZMknSZpuKQ7c3GHSpqZlk+W9IikpZKmS+pVpi4XSVqZjjkhbauTtFDSCkn3SNojbZ8raVBa7itpTVoeK+luSbMkPS3p6rR9AtAj1XtKrt03AEuByyRdl6vLOEnXljltJwJbIuKt6e4jYllEPJySM9+X9NvUltGNtPNtPUwk3StpaFreKOnfJT0m6X5JH0lt/b2kkbkw/VIbfyfpilysjen/XpIeSOd8paRPl2mLmZmZmZmZFaRzlR9tIWlPSXPSd+U5Dd+vS8r0k/Tr9H35CUlfqyR2h0yoJPsD10fEwcArwKnA3RExOCIOB1YB50bEAmAGcGFE1EXEM7kYc4BjJO2a1kcD0yT1BS4FhkXEkcAS4JulFZA0nKz3y9HpmFenXZOBb0XEYcBK4IrS5zaiLh3/UGC0pH4RcTHweqr3mFTuAGByRBwBXAOMlNQ17TsbuLVM/EOAx8rs+2w6/uHAMLLk0z4V1LnBrmQ9X44CNgD/CnwC+AxwVa7cR4Ax6Vifa0gu5bwBfCad8xOBH6SeNW8jabykJZKWPD1zZguqaWZmZmZmZu8wFwMPRMT+ZDdOubiRMvXAv0TE3wLHAF/KT/tRTkdOqDwbEcvS8mNAf+AQSQ9LWkn2xf3gpgJERD0wCxghqQvwKeAXZC/AQcB8ScuAs4B9GwkxDLg1Il5L8dZK2h3oExHzUpnbgBMqaM8DEbE+It4AnixzPIDnImJhOt4m4EHgFEkHAl0jYmUFxyr1MWBqRGyNiL8A84DBLXj+ZrLzCFkCaV5EbEnL/XPl5kTEyxHxOnB3Om6egO9JWgHcD3yARoYkRcTEiBgUEYP2HzGiBdU0MzMzMzOzUjU+h8qnyb5Xk/4fVVogIl6IiKVpeQNZB4sPNBe4I9/l583c8lagBzAJGBURyyWNBYZWEGca8CVgLbA4IjakXhFzIuL0fEFJRwM3pdXLyRIALbm/VD3bk2DdS/aVtqfca7upZP0WsrlQVlO+dwrAE8BpZfZVconn6w5vr/+WiGg4D9tIbYmIbSlR1aD0XJWujwHeAxwVEVvSkKjS82RmZmZmZmbvIJLGA+NzmyZGxMQKn753RLwAWeJE0nubOVZ/4AhgUXOBO3IPlcb0Bl5IQ2DG5LZvSPsaMxc4EhhHllwBWAgcJ2k/gDQXy8CIWJSG39RFxAxgNnCOpJ6p3J4RsR5YJ+n4FOtMsh4fAGuAo9JyueRGqS25IT07iIhFQD/gDGBqE3EeBHaRNK5hg6TBkoYAD5ENM+os6T1kPWoeLXn+GqBOUidJ/ciG77TUJ9L4tx5kWcX5Jft3B15MyZQTKd9Lx8zMzMzMzArSSVHVR36UQXq8LZmS5uL8bSOPFs2rmeY+vQv4ekS82lz5jtxDpTGXkWWhniMbbtKQRLkDuFnSVylJZETEVkn3AmPJhvYQES+lHi5TJe2Sil4KPFXy3FmS6oAlkjYDvyLrLXIWcGNKtPyebG4TyOY8uVPSmWQJjkpMBFZIWgp8u0yZO4G6iFhXLkhEhKTPAP9X0sVk85WsAb5OllD5KLCcrNfIRRHx55TZazAfeJbsvP6WbFLclvoNcDuwH/DfEbGkZP8UYKakJcAysl43ZmZmZmZm9i4WEcPK7ZP0F0n7pN4p+wAvlinXlSyZMiUi7q7kuNo+0sI6qpQQui4iHmjvuuxs/zBvXqFvgO6di30/1W8r9qbr2wp+u28rNpy7zLVRAeNL36bo66Ua3ij4PdKtU7GN3lxw/XoW/BlT9GtcdHuL/kwtur1Fv+c6qdgKvvRGW++L8HZ7dCv6U7/2P7emn3lj84Va4HO3n1doPGubWr/+7N1v8pAhBV+F7ecnv7uvqu+Acw/4u1afK0nfB16OiAmpc8CeEXFRSRmRza+yNiK+Xmls91DpwCT1IRuas7wjJlOg+C/wRf/yv7HohEqh0aBLB/tFpKP94lV0ezcX/12MrQW/Ryg4oVJ0gqboL9yVTYFVuaLrV/R7pL7geN0Kvvxeqy/2p9LGLcXG67drfaHxAN7YWvQfDoqNV3QCxAkaM7N2MYFspMe5wP8CnwOQ9H7gloj4JHAc2XQbK9ONZQAuiYhfNRXYCZUOLCJeAQbmt0nai+xWUqVOioiXd0a9zMzMzMzM7J2j6D/EFSl9jz2pke1/Aj6Zln9DK/7S5ISKvU262Oraux5mZmZmZmZmtazwKQskjZJ0UG79KkllJ4ipJZIuKTDWVknLJD0habmkb0oq5HxXck4ljUzjw6pG0hpJKyWtkDRPUuF31UnH6NvI9islXZCW3zHXmJmZmZmZ2btNJ1X3UauqMQfkKOCthEpEXB4R91fhONXQaEJFmZaeq9fT7ZEPBj5B1pXoirZWECo7pxExIyImFHG8ZpwYEYeR3T760p1wvB28w64xMzMzMzMzexdoNkkgqb+kVZJuTr0tZkvqIWmcpMWp98VdknpKOhYYCXw/9c4YIGmSpNMkDZd0Zy7uUEkz0/LJkh6RtFTS9HTv58bqclHqEbFc0oS0rU7SwtRL4h5Je6TtcyUNSst9Ja1Jy2Ml3S1plqSnJV2dtk8AeqR6T8m1+wayW/xeJum6XF3GSbq2kpMcES8C44Evp+RMd0m3prY8LunEXN1+LmmmpGclfTn1bHk8tXHPVG6SpNPS8hpJ30nnbqWkA3OxfpyW907nZnl6HJu2/1zSY+l1HZ9r20ZJ/yeVXShp7wqa+QjwgfT896RrYnF6HJe2XynpdkkPpnM/Lm0fquxOQw3H/7Gy2043uFDSo+mxX+mBS87HYEkLUt0fldS7tLyZmZmZmZkVxz1UmrY/cH3qbfEKcCpwd0QMjojDgVXAuRGxAJgBXJh6ZzyTizEHOEbSrml9NDBN2XCOS4FhEXEksAT4ZmkFJA0n6/1ydDrm1WnXZOBbqZfESirrBVKXjn8oMFpSv4i4mO29SsakcgcAkyPiCOAaYKSye1MDnA3cWsGxAIiI35Od7/cCX0rbDgVOB26T1D0VPQQ4A/gI8H+A19LxHwG+WCb8X9O5+0/ggkb2/xCYl87bkcATafs5EXEUMAj4qrIJaQF2BRam8g8B4ypo4t8DP0/L/0F2G+bBZNfKLblyhwGfAj4KXK5sZuXmvBoRHwF+DPzfcoUkdQOmAV9LdR8GvN5IufGSlkha8tTMmRUc3szMzMzMzOztKp2U9tmIWJaWHwP6A4dI+legD9ALuK+pABFRL2kWMELSz8i+VF8EDCEbIjRfEkA3suRBqWHArRHxWoq3VtLuQJ+ImJfK3AZMr6A9D0TEegBJTwL7As83Uu65iFiYjrdJ0oPAKZJWAV0jYmUFx8pryK19DPhRirta0nNsv9vOryNiA7BB0nqg4Rv/SrJkRGPuTv8/Bny2kf0fJyVjImIrsD5t/6qkz6TlfmSJs5eBzUBDj5HHyIYslfPr1IPlRbYP+RkGHJReT4Ddcj1FfhERrwOvS/o1WeLolSbiA0zN/X9dE+UOAF6IiMUAEfFqY4UiYiIwEeCL8+bV+I1rzczMzMzMaltndcyvVZUmVN7MLW8FegCTgFERsTwNzxhaQZxpZL0z1gKLI2KDsm/dcyLi9HxBSUcDN6XVy8mSES15lerZ3gOne8m+0vaUOw+bStZvIZtnZTUt6J0CIOnD6Vgv0vTtmPJ125Zb39ZEPRvKNNWW0voMJUt8fDQiXpM0l+3naUtENJzrrUAXSZ3JkisAMyLi8rR8Itl5mgRcRda7qFOK+7beISnBUvoaBm9/rWDH1yvKLO/QrGb2m5mZmZmZmRWiLZPS9gZeSENgxuS2b0j7GjOXbMjJOLLkCsBC4LiGuTGUzcUyMCIWpeE3dRExA5gNnCOpZyq3Z+plsk7S8SnWmUBDb5U1wFFp+bQK27QlN6RnBxGxiKwnxxls7zXRLEnvAW4EfpwSFQ+RzpmkgcAHgd9VGq8VHgDOT8frLGk3YHdgXUqmHAgc01SAiNiaez0uL9n3OvB14ItpnpfZwJcb9kuqyxX/dJpDZi+yJNxi4DmyHi27pF5HpfcIH537v7HeSw1WA++XNDgdt7ck3xrczMzMzMysijpV+VGr2vJl8zJgEdmX4ZVsT6LcAdws6auUJDIiYmuafHQscFba9lLq4TJV0i6p6KXAUyXPnZW+mC+RtBn4FVlvkbOAG1Oi5fdkc5tANufJnZLOBB6ssE0TgRWSlgLfLlPmTqAuItY1E6uHpGVAV7IeGLcDDZPY3pDqvDLtGxsRb+aGyBTta8BESeeS9Tg5H5gFnCdpBVkyZ2FbDhARL0iaStYD6avA9Sl2F7IE0nmp6KPAL8mSSN+NiD8BKJuweAXwNPB4SfhdJC0iey+dThkRsVnSaOBHknqQzZ8yDNjYlraZmZmZmZlZebU8cWw1afvIDqtESghdFxEPtHdd3mkkXQlsjIhr2rsuDYqeQ2W3btuKDMfGLcXmY+sLfrt3KfiDc1uNfxwV/YOi6PbW+g+yzcW+PQB4c2ux75EeXapQyQJ161TsRbMtir1oin6Ni/6MKfozsFvBfzJ7Y2uxDf7L650Ljfe3fTYXGg+Kb3P9ttr+IJx+5o2Fxvvc7ec1X8jKqvWf6/buN3nIkNr+0GqBO38/q6rvgM9/+O9r8lx5OESFJPUh612x3MkUK6foXwyLVvSXsVr/xbVotf6LUqeCJwMr+st20V+OAehc7Df4ot/DPTt3rPfc5oLr16Xg81eVa7BAf36t2ATIB3vVFxqvd9fiE46v1XesX0WLToA4QWNmtaLW/7BXLR3rp1gbRMQrbL8TDwBpHpDGkisnRcTLO6Ne7yQRcWV718HMzMzMzMysCE6otEFKmtS1dz3MzMzMzMzM2ktHvW1yLU+Ya2ZmZmZmZmZWk5xQqQGSRkk6KLd+laRh7VmnSkm6pMBYG0vWx0r6cTPPGSnp4rT8jj2PZmZmZmZm71SdVN1HrXJCpTaMAt5KBETE5RFxf/tVp0UaTagoU/XrKyJmRMSEtDqKd+55NDMzMzMzs3cQJ1SqQFJ/Sask3SzpCUmzJfWQNE7SYknLJd0lqaekY4GRwPclLZM0QNIkSadJGi7pzlzcoZJmpuWTJT0iaamk6ZJ6lanLRZJWpmNOSNvqJC2UtELSPZL2SNvnShqUlvtKWpOWx0q6W9IsSU9LujptnwD0SPWekmv3DcBS4DJJ1+XqMk7Sta08pyMkLZL0uKT7Je2dq9uPmzqPjcQaL2mJpCVPzZzZmuqYmZmZmZlZ4h4qVrT9gesj4mDgFeBU4O6IGBwRhwOrgHMjYgEwA7gwIuoi4plcjDnAMZJ2TeujgWmS+gKXAsMi4khgCfDN0gpIGk7Wa+PodMyr067JwLci4jBgJXBFBe2pS8c/FBgtqV9EXAy8nuo9JpU7AJgcEUcA1wAjJXVN+84Gbm3iGA3JmWWSlgFX5fb9Bjgmxb0DuCj/xGbOIyVlJ0bEoIgYNHDEiAqabmZmZmZmZvZ2vstP9TwbEcvS8mNAf+AQSf8K9AF6Afc1FSAi6iXNAkZI+hnwKbJEwhCyoS3zJQF0Ax5pJMQw4NaIeC3FWytpd6BPRMxLZW4DplfQngciYj2ApCeBfYHnGyn3XEQsTMfbJOlB4BRJq4CuEbGyiWO8HhF1DSuSxgKD0urfkCWT9kntfbaCOpuZmZmZmVmV1XIvkmpyQqV63swtbwV6AJOAURGxPCULhlYQZxrwJWAtsDgiNijLosyJiNPzBSUdDdyUVi8HBLTk/lX1bO+11L1kX2l7yl07m0rWbyGbZ2U1TfdOac6PgGsjYoakocCVbYhlZmZmZmZm1iYe8rNz9QZeSENgxuS2b0j7GjMXOBIYR5ZcAVgIHCdpP4A0F8vAiFiUhrvURcQMYDZwjqSeqdyeqZfJOknHp1hnAg29VdYAR6XlHeYeKWNLbkjPDiJiEdAPOAOYWmHMxuwO/DEtn1WmTFPn0czMzMzMzKqgs6r7qFVOqOxclwGLyOZGWZ3bfgdwYZpwdUD+CRGxFbgXGJ7+JyJeAsYCUyWtIEuwHFh6sIiYRTavyJI0J8kFaddZZJO3riCbG6VhrpJrgPMlLQD6VtimicAKSVOaKHMnMD8i1lUYszFXAtMlPQz8tUyZsufRzMzMzMzMrEiKaMmIELOWk3QvcF1EPNDedSk19qF5hb4BttX422lbwfE6Wka26LGhmwt+QbrUcPa+WnxN15aiX49av6brC/7ML7q9Rf9MKvr1Bb/nas30M28sNN7nbj+v0Hhm7zaThwyp8Z90lZv9x19V9ZvQyR/4ZE2eK/8cs6qR1EfSU2STzdZcMsXMzMzMzMystTwprVVNRLwCDMxvk7QX0Fhy5aSIeHln1MvMzMzMzMyK01F7ajihYjtVSprUtXc9zMzMzMzMzNqioyaSqkrSKEkH5davkjSsPetUKUmXFBhrYwvL95f024KOPTTN3WJmZmZmZmZV1EnVfdQqJ1SqYxTwVkIlIi6PiPvbrzot0mhCRZk2Xy+SOrc1hpmZmZmZmVl7c0KlAqnnxCpJN0t6QtJsST0kjZO0WNJySXdJ6inpWGAk2W2Jl0kaIGmSpNMkDZd0Zy7uUEkz0/LJkh6RtFTSdEm9ytTlIkkr0zEnpG11khZKWiHpHkl7pO1zJQ1Ky30lrUnLYyXdLWmWpKclXZ22TwB6pHpPybX7BmApcJmk63J1GSfp2grO31BJv5b038BKSZ0lfT+duxWS/qnMOX84nY+l6bw2xJor6WeSVqd6Ku37+7TtN8Bnm6uXmZmZmZmZtV1nVfdRq5xQqdz+wPURcTDwCnAqcHdEDI6Iw4FVwLkRsQCYAVwYEXUR8UwuxhzgGEm7pvXRwDRJfYFLgWERcSSwBPhmaQUkDSfr/XJ0OubVaddk4FsRcRiwEriigvbUpeMfCoyW1C8iLia7I09dRIxJ5Q4AJkfEEcA1wEhJXdO+s4FbKzgWwEeAb0fEQcC5wPqIGAwMBsZJ+lBJ+ReBT6TzMRr4YW7fEcDXyXoBfRg4TlJ34GZgBHA88L5yFZE0XtISSUuemjGzwuqbmZmZmZmZbedJaSv3bEQsS8uPAf2BQyT9K9AH6AXc11SAiKiXNAsYIelnwKeAi4AhZMmB+amzRTfgkUZCDANujYjXUry1knYH+kTEvFTmNmB6Be15ICLWA0h6EtgXeL6Rcs9FxMJ0vE2SHgROkbQK6BoRKys4FsCjEfFsWj4ZOEzSaWl9d7KE1VO58l2BH0uqA7by9rsFPRoRf0h1X0b2Wmwke42eTtv/CxjfWEUiYiIwEWDsQ/Oqer90MzMzMzOzd7tO6phfq5xQqdybueWtQA9gEjAqIpZLGgsMrSDONOBLwFpgcURsSENW5kTE6fmCko4GbkqrlwMCWnKl1rO9F1L3kn2l7Sl3LWwqWb+FbJ6V1VTeO6U0joCvRMTbElCS+udWvwH8BTicrA1v5PaVq3vHfBebmZmZmZm1o1qeOLaaPOSnbXoDL6QhMGNy2zekfY2ZCxwJjCNLrgAsJBu2sh9AmotlYEQsSsNv6iJiBjAbOEdSz1Ruz9TLZJ2k41OsM4GG3iprgKPSckNvkOZsyQ3p2UFELAL6AWcAUyuMWeo+4PyG40gamBsG1WB34IWI2EbWpuYms10NfEjSgLR+elOFzczMzMzMzNrCCZW2uQxYRDY3yurc9juACyU9nvuCD0BEbAXuBYan/4mIl4CxwFRJK8gSLAeWHiwiZpHNz7IkDXW5IO06i2wS3BVkc6NclbZfQ5a4WAD0rbBNE4EVkqY0UeZOYH5ErKswZqlbgCeBpcpuk3wTO/aQuQE4S9JCsuE+pT1l3iYi3iAb4vPLNCntc62sm5mZmZmZmbVAR71tsiI8SsJaRtK9wHUR8UB716WtvvDrhwp9A+zWdVuR4Xhjaw1/egDFtrb2M7ybtxX7evTsUuwZrC+4fkX/8KrvgD9uir6mi37PdSn4Nd5W8Gtc69dg0eev6PHnRX8mFP0ZCMW3uejXxNpm+pk3Fhrvc7efV2i8oj9jiv4MtHe/yUOGvGs+tR558ZdVfQd89L2fqslzVevfX6yGSOoj6SmyOwG945MpZmZmZmZm1nadqvyoVZ6U1ioWEa/w9rvtIGkvoLHkykkR8fLOqJeZmZmZmZnZzuaEirVJSprUtXc9zMzMzMzMrH2oJgfkVF8t957pUCSNknRQbv0qScPas06VknRJgbE2pv/7SzqjgvL908S2ZmZmZmZmZjuNEyq1YxTwVkIlIi6PiPvbrzot0mhCRZnWXmP9yW7NbGZmZmZmZjVMVX7UKidUqiT1nFgl6WZJT0iaLamHpHGSFktaLukuST0lHQuMJLv18TJJAyRNknSapOGS7szFHSppZlo+WdIjkpZKmi6pV5m6XCRpZTrmhLStTtJCSSsk3SNpj7R9rqRBabmvpDVpeaykuyXNkvS0pKvT9glAj1TvKbl23wAsBS6TdF2uLuMkXVvBKZwAHJ/ifiPFfTi1dWk6Z6XtfFhSXW59vqTDKjiWmZmZmZmZWYs4oVJd+wPXR8TBwCvAqcDdETE4Ig4HVgHnRsQCYAZwYUTURcQzuRhzgGMk7ZrWRwPTJPUFLgWGRcSRwBLgm6UVkDScrPfL0emYV6ddk4FvRcRhwErgigraU5eOfygwWlK/iLiY7K4/dRExJpU7AJgcEUcA1wAjJXVN+84Gbq3gWBcDD6e41wEvAp9IbR0N/LCR59wCjE3tHgjsEhErSgtJGi9piaQlz9w7o4KqmJmZmZmZWTlSdR+1ygmV6no2Ipal5cfIhrEcknpSrATGAAc3FSAi6oFZwAhJXYBPAb8AjiEbIjRf0jLgLGDfRkIMA26NiNdSvLWSdgf6RMS8VOY24IQK2vNARKyPiDeAJ8scD+C5iFiYjrcJeBA4RdKBQNeIWFnBsUp1BW5O5206ueFROdPTcboC5wCTGgsUERMjYlBEDBpwyshWVMXMzMzMzMw6Ot/lp7rezC1vBXqQfckfFRHLJY0FhlYQZxrwJWAtsDgiNkgSMCciTs8XlHQ0cFNavZxsyFm0oM71bE+0dS/ZV9qectfPppL1W8jmWVlNZb1TGvMN4C/A4al+b5QWiIjXJM0BPg18HhjUymOZmZmZmZlZhTpqT42O2u721Bt4IfWiGJPbviHta8xc4EhgHFlyBWAhcJyk/QDSXCwDI2JRGiZTFxEzgNnAOZJ6pnJ7RsR6YJ2k41OsM4GG3iprgKPS8mkVtmlLbkjPDiJiEdCPbJLZqRXGLD0fuwMvRMS2VN/OZZ53C9lwoMURsbbCY5mZmZmZmZm1iBMqO99lwCKyuVFW57bfAVwo6XFJA/JPiIitwL3A8PQ/EfES2XwhUyWtIEuwHFh6sIiYRTY/y5I0NOiCtOsssklwV5DNjXJV2n4NcL6kBUDfCts0EVghaUoTZe4E5kfEugpjrgDq00S63wBuAM6StBAYyI69YACIiMeAV2l9TxgzMzMzMzNrASmq+mhb3bSnpDnp5ipzGm7IUqZs5/Sd/N6KYke0rXJmlUgX5HUR8UCVj/N+sh49B6beLE364rx5hb4BunQq9v1Uv62GZ2ACOhVcvW01/nHU7AXVQkVntN8Jr0fRddxYX2zAnp1r+yKs9de41t8jRddvzSvlOku2zn57bC00HhT/GvfpVuxZfGWz/7ZnlZt+5o2Fxvvc7ecVGs+spSYPGVLbv+y3wOMv31vVX6KO2OuUVp+rdIfatRExQdLFwB4R8a0yZb9JNnXEbhFxSnOx/VPMqkpSH0lPkd0JqNrJlC+S9f75diXJFDPbuYpOBphZ02o9mWJmZu8eqvKjjT5NdiMW0v+jGm2D9DdkN4G5pdLAnpTWqioiXiEbovMWSXsBjSVXToqIl9twrMlkt4M2MzMzMzMzA9g7Il4AiIgXJL23TLn/C1xE+blNd+CEiu10KWlS1971MDMzMzMzs7ZTlXsiSxoPjM9tmhgRE3P77wfe18hTv11h/FOAFyPiMUlDK62XEyo1QNIo4KmIeDKtXwU8FBH3t2vFKiDpkoj4XkGxNkZEryJimZmZmZmZ2btDSp5MbGL/sHL7JP1F0j6pd8o+wIuNFDsOGCnpk0B3YDdJ/xUR/9BUvTyHSm0YBRzUsBIRl78TkinJJY1tVMbXl5mZmZmZ2btcjc+hMoPsLrek/39RWiAi/r+I+JuI6A98AXiwuWQKOKFSFZL6S1ol6WZJT0iaLamHpHGSFqdbAd8lqaekY4GRZLcwXiZpgKRJkk6TNFzSnbm4QyXNTMsnS3pE0lJJ0yU12rND0kWSVqZjTkjb6iQtlLRC0j0Nt42SNFfSoLTcV9KatDxW0t2SZqVbTV2dtk8AeqR6T8m1+wZgKXCZpOtydRkn6doWnssRkhalW1fdL2nvtH1PST9PbVgo6bC0fUiqz7L0nIrHv5mZmZmZmVnLdVJ1H200AfiEpKeBT6R1JL1f0q/a1O42V83K2R+4PiIOBl4BTgXujojBEXE4sAo4NyIWkGXMLoyIuoh4JhdjDnCMpF3T+mhgmqS+wKXAsIg4ElgCfLO0ApKGk/V+OTod8+q0azLwrYg4DFgJXFFBe+rS8Q8FRkvqFxEXk929py4ixqRyBwCTI+II4BqyblNd076zgVsrOFbeb4BjUrw7yCYJAvgO8HhqwyVsn4z2AuBLEVEHHA+8XhpQ0nhJSyQteWrmzBZWx8zMzMzMzN4pIuLliDgpIvZP/69N2/8UEZ9spPzcSm6ZDJ5DpZqejYhlafkxoD9wiKR/BfoAvYD7mgoQEfWSZgEjJP2M7BZOFwFDyIYIzVc2+0834JFGQgwDbo2I11K8tZJ2B/pExLxU5jZgegXteSAi1gNIehLYF3i+kXLPRcTCdLxNkh4ETpG0CugaESsrOFbe35AlkfYha+ezafvHyJJURMSDkvZKbZsPXCtpClkC6w+lAfPj7744b15V75duZmZmZmb2blflOWlrlnuoVM+bueWtZMmrScCXI+JQsh4W3SuIMw34PPBxYHFEbCC7XuekniF1EXFQRJwr6ejccJeRqVxLEgb1bL8mSuvWWHsas6lk/RZgLK3rnQLwI+DH6Zz9U65ejb1nIyImAP8I9AAWSjqwFcc0MzMzMzMza5ITKjtXb+CFNARmTG77Bsrf63oucCQwjiy5ArAQOE7SfgBpLpaBEbEol2SZAcwGzpHUM5XbM/UyWSfp+BTrTKCht8oa4Ki0fFqFbdqSG9Kzg4hYBPQDzgCmVhgzb3fgj2n5rNz2h0jnMN3W6q8R8aqkARGxMiL+nWwolBMqZmZmZmZmVSRV91GrnFDZuS4DFpHNjbI6t/0O4MI0ieqA/BMiYitwLzA8/U9EvETW62OqpBVkCZYdEgcRMYtsfpYlkpaRzS8CWWLi++m5dcBVafs1wPmSFgB9K2zTRGBFGmJTzp3A/IhY10ysnpL+kHt8E7gSmC7pYeCvubJXAoNSGyawPdnydUm/lbScbP6U/6mwHWZmZmZmZmYVU4SnkLDqknQvcF1EPNDedSlV9BwqXToV+36q31bD6VgKmXH7bbbV+MfRtoLjFZ3RrvXXo+j6AWysLzZoz861fRHW+mtc6++Rouu35pXOhcbbb4+thcYr+vXt063oMwivbPbf9qxy08+8sdB4n7v9vELjmbXU5CFDavuX/RZY9cq9Vf0l6m/7nFKT58qT0lrVSOoDPAosr8VkCsBuBf9yOLjv5kLjPfyXXQqNt3lrsZ9DnVTs5+a2qMnPyaop+qtJry7FRnxmQ9nRfK3SvcaTFQCvFRxva8FJ0X161hca742CPxOeLfiaGbDblkLjFZ2kPnCvYl+PbgUn5Ve90q3QeJu3FZvwAagv+GOhV5fa/5ypZbWeSC86AdIREzS1npjvaPWzdz4nVKxqIuIVYGB+m6S9gMaSKydFxMs7o15mZmZmZmZWnI6aa3JCxXaqlDSpa+96mJmZmZmZmbWFEypmZmZmZmZm1moddTiUZwKrAkmjJB2UW79K0rD2rFOlJF1SYKyQdHtuvYukl9Ikta2Jd56kLxZVPzMzMzMzM7PWckKlOkYBbyVUIuLyiLi//arTIo0mVJRp6fWyCThEUo+0/gngj62tWETcGBGTW/t8MzMzMzMzK56q/KhVTqhUQFJ/Sask3SzpCUmzJfWQNE7SYknLJd0lqaekY4GRwPclLZM0QNIkSadJGi7pzlzcoZJmpuWTJT0iaamk6ZJ6lanLRZJWpmNOSNvqJC2UtELSPZL2SNvnShqUlvtKWpOWx0q6W9IsSU9LujptnwD0SPWekmv3DcBS4DJJ1+XqMk7Stc2cvv8BPpWWTwem5p6/q6SfpnP4uKRPp+0/lHR5Wv47SQ9J6iTpSkkXpO37Sbo/nYel6TxL0vcl/Tado9FlzuF4SUskLXni563qLGNmZmZmZmYdnBMqldsfuD4iDgZeAU4F7o6IwRFxOLAKODciFgAzgAsjoi4insnFmAMcI2nXtD4amCapL3ApMCwijgSWAN8srYCk4WS9X45Ox7w67ZoMfCsiDgNWAldU0J66dPxDgdGS+kXExcDrqd5jUrkDgMkRcQRwDTBSUsN9Mc8Gbm3mOHcAX5DUHTgMWJTb923gwYgYDJxIloTaFbg41elE4IfA2RFRej/YKWSvx+HAscALwGdTuw4HhqV4+5RWKCImRsSgiBh08KhTmqm+mZmZmZmZNUWKqj5qlRMqlXs2Ipal5ceA/mTDWR6WtBIYAxzcVICIqAdmASMkdSHrufEL4BiyIULzJS0DzgL2bSTEMODWiHgtxVsraXegT0TMS2VuA06ooD0PRMT6iHgDeLLM8QCei4iF6XibgAeBUyQdCHSNiJXNtHkF2bk6HfhVye6TgYtTm+cC3YEPpvaNI0tA/bgkKYWk3sAHIuKedIw30nM+BkyNiK0R8RdgHjC42TNhZmZmZmZm1kK+y0/l3swtbwV6AJOAURGxXNJYYGgFcaYBXwLWAosjYoMkAXMi4vR8QUlHAzel1cvJho+1JD1Xz/akWfeSfaXtKXctbCpZv4VsnpXVNN87pcEMst4tQ4G9ctsFnBoRv2vkOYcCLwPvb2RfuWF0tTy8zszMzMzM7F2po34Rcw+VtukNvJCGwIzJbd+Q9jVmLnAkWQ+MaWnbQuA4SfsBpLlYBkbEojT8pi4iZgCzgXMk9Uzl9oyI9cA6ScenWGeS9cwAWAMclZZPq7BNW3JDenYQEYuAfsAZ5OZDacZPgasa6c1yH/CVlFBC0hHp/32BfwGOAIanxFK+Dq8Cf5A0KpXfJZ2Th8iGCnWW9B6ynjqPVlhHMzMzMzMzs4o5odI2l5HNCTKHrMdGgzuAC9NEqwPyT4iIrcC9wPD0PxHxEjAWmCppBVmC5cDSg0XELLLeHkvSMJkL0q6zyOYLWUE2h8hVafs1wPmSFgB9K2zTRGCFpClNlLkTmB8R6yoJGBF/iIj/aGTXd4Gu6Xi/Bb6bkis/AS6IiD8B5wK3pDlY8s4EvpravAB4H3APsAJYTjY06aKI+HMldTQzMzMzM7PWkar7qFWKqN0JXqw2SboXuC4iHmjvurTVF+fNK/QN0K1zse+n+m01/OkBbPPHR03p0qnYF2RbFHv91Vfhein6GuxU8Fuu6Pp1K/jPIEXX77WtxZ7AngV/ppbObt5WRf9Vqujrb2N9bb8eUPvvObOWmH7mjYXG+9zt5xUaz2rP5CFDavuX/Rb4/YaZVf0E/nDvETV5rtxDxSomqY+kp8juBPSOT6aYmZmZmZlZ23Wq8qNWeVJaq1hEvAIMzG+TtBfQWHLlpIh4eWfUy8zMzMzMzNpPLQ/LqSYnVKxNUtKkrr3rYWZmZmZmZrYz1XLvmXYjaZSkg3LrV0ka1p51qpSkSwqMFZJ+kFu/QNKVafk8SV8s6lhmZmZmZmb2zqQqP2qVEyqNGwW8lVCJiMsj4v72q06LNJpQUaalr/ebwGcl7XCHoIi4MSImt6aCbayTmZmZmZmZWbvrEF9mJfWXtErSzZKekDRbUg9J4yQtlrRc0l2Seko6FhhJdhviZZIGSJok6TRJwyXdmYs7VNLMtHyypEckLZU0XVKvMnW5SNLKdMwJaVudpIWSVki6R9IeaftcSYPScl9Ja9LyWEl3S5ol6WlJV6ftE4Aeqd5Tcu2+AVgKXCbpulxdxkm6tolTV092G+VvNNKOKyVdkJb3k3R/atPSdM56SXogra+U9OmS16KhTv0k/aekJem1+U7uGJ+UtFrSbyT9MN1d6G3HTuu/ldQ/Lf9c0mMp1vgm2mZmZmZmZmYF6Ki3Te4QCZVkf+D6iDgYeAU4Fbg7IgZHxOHAKuDciFgAzAAujIi6iHgmF2MOcIykXdP6aGBa6sFxKTAsIo4ElgDfLK2ApOFkvV+OTse8Ou2aDHwrIg4DVgJXVNCeunT8Q4HRkvpFxMVkd+Cpi4gxqdwBwOSIOAK4BhgpqWvadzZwazPHuR4YI2n3JspMITu3hwPHAi8AbwCfSefjROAH0ltvhbfqFBHPAd+OiEHAYcAQSYdJ6g7cBAyPiI8B76ngnACcExFHAYOAr6ZJc99G0viUwFny1MyZFYY1MzMzMzMz264jJVSejYhlafkxoD9wiKSHJa0ExgAHNxUgIuqBWcAISV2ATwG/AI4hGyI0X9Iy4Cxg30ZCDANujYjXUry1KVHRJyLmpTK3ASdU0J4HImJ9RLwBPFnmeADPRcTCdLxNwIPAKZIOBLpGxMpm2vwqWcLnq43tl9Qb+EBE3JPKv5HaJ+B7klYA9wMfAPYurVPyeUlLgcfJXoODgAOB30fEs6nM1KbqmfNVScuBhUA/skRaaZsmRsSgiBg0cMSICsOamZmZmZlZYzrqHCod6S4/b+aWtwI9gEnAqIhYLmksMLSCONOALwFrgcURsSH1vJgTEafnC0o6mqyXBcDlZNdCtKDO9WxPenUv2VfannKv5aaS9VvI5llZTfO9Uxr8X7LhOY2VL3d9jyHrVXJURGxJw5Ua2vBWnSR9CLgAGBwR6yRNSuWaet/kzwsNcSUNJUtafTQiXpM0lx3Pm5mZmZmZmVmbdaQeKo3pDbyQhsCMyW3fkPY1Zi5wJDCOLLkCWW+I4yTtB5DmYhkYEYvS8Ju6iJgBzAbOkdQzldszItYD6yQdn2KdCTT0VlkDHJWWT6uwTVtyQ3p2EBGLyHpunEGFvT4iYi1wJ3BuI/teBf4gaRSApF1S+3YHXkzJlBMp34NmN7IEy3pJewPD0/bVwIcb5kYhG97UYA3Za4CkI4EPpe27A+tSMuVAsp5DZmZmZmZmVkWdVN1HreroCZXLgEVkc6Oszm2/A7hQ0uOSBuSfEBFbgXvJvvjfm7a9BIwFpqYhLgvJhqxQ8txZZPOzLElDgxomVj2LbBLcFWRzo1yVtl8DnC9pAbDDnXbKmAiskDSliTJ3AvMjYl2FMQF+0EQdziQbarMCWAC8j2xelUGSlpAlq1Y39sSIWE421OcJ4KfA/LT9deCfgVmSfgP8BVifnnYXsGc6h+cDT6Xts4AuqR7fJXsdzMzMzMzMzAqniJaMQLF3g3S3nOsi4oH2rktTJPWKiI1pSNX1wNMRcV1zz2uJL86bV9NvgFrOxgJsq+mzV7yiX4+iz1+tXy/174DrpejXpEvR10yx4Qr/q0rR9Sta0e2t9ffc+s3Ftrh311p/hc3eXaafeWPhMT93+3mFx7TWmzxkSI3/JKncC6/NrOpvevv0HFGT56qj91DpUCT1kfQU2Z2AajqZkoxLvVCeIBvOc1PTxc3MzMzMzMx2jo40KW2HFxGvAAPz29JthRtLrpwUES/vjHqVk3qjFNojxczMzMzMzIolvQO6IleBEyodXEqa1LV3PczMzMzMzMzeSTzkp0ZIGiXpoNz6VZKGtWedKiXpkgJjbZW0TNJvJU1vuCOSmZmZmZmZ1SZV+VGrnFCpHaOAtxIqEXF5RNzfftVpkUYTKsq09Bp7Pd1m+hBgM1DRzFmS3NvKzMzMzMzMdhonVKpEUn9JqyTdLOkJSbMl9ZA0TtJiScsl3SWpp6RjgZFkt05eJmmApEmSTpM0XNKdubhDJc1MyydLekTS0tSbo1eZulwkaWU65oS0rU7SQkkrJN0jaY+0fa6kQWm5r6Q1aXmspLslzZL0tKSr0/YJQI9U7ym5dt8ALAUuk3Rdri7jJF1b4Wl8GNhP0q6SfprO2+OSPp2r0/R0Pmanc3Nv7lg/ljS2wmOZmZmZmZlZK0jVfdQqJ1Sqa3/g+og4GHgFOBW4OyIGR8ThwCrg3IhYAMwALky9M57JxZgDHCNp17Q+GpgmqS9wKTAsIo4ElgDfLK2ApOFkvV+OTse8Ou2aDHwrIg4DVgJXVNCeunT8Q4HRkvpFxMVs71UyJpU7AJgcEUcA1wAjJXVN+84Gbm3uQKnHyfBUt28DD0bEYOBEssRTw/n4KHBWRHy8gvo3xB4vaYmkJU/NnFnp08zMzMzMzMze4mES1fVsRCxLy48B/YFDJP0r0AfoBdzXVICIqJc0Cxgh6WfAp4CLgCFkQ4TmK0vZdQMeaSTEMODWiHgtxVsraXegT0TMS2VuA6ZX0J4HImI9gKQngX2B5xsp91xELEzH2yTpQeAUSauArhGxsolj9Ei3Soash8pPgAVkSZkL0vbuwAfT8pyIWFtB3d8SEROBiQBfnDevY05HbWZmZmZmVpAa7kRSVU6oVNebueWtQA9gEjAqIpan4ShDK4gzDfgSsBZYHBEblGVR5kTE6fmCko4Gbkqrl5Nd2y1JGtSzvedS95J9pe0pd/1sKlm/hWyeldU03zvl9Yioy29IbT01In5Xsv3okmPl6w471t/MzMzMzMysEB7ys/P1Bl5IQ2DG5LZvSPsaMxc4EhhHllwBWAgcJ2k/gDQXy8CIWJSG39RFxAxgNnBOw91yJO2Zepmsk3R8inUm0NBbZQ1wVFo+rcI2bckN6dlBRCwC+gFnAFMrjJl3H/CVlFhB0hFlyj0HHCRpl9QL56RWHMvMzMzMzMxaoFOVH7Wqluv2bnUZsIhsbpTVue13ABemSVcH5J8QEVuBe8nmFLk3bXsJGAtMlbSCLMFyYOnBImIW2fwsS9JQmoZhM2eRzUWygmxulKvS9muA8yUtAPpW2KaJwApJU5oocycwPyLWVRgz77tA13SM36b1HUTE8+k4K4ApwOOtOJaZmZmZmZm1QEedlFYRnkLCqi/dfee6iHigveuS9w8Fz6HSp9u2IsOxcUuxOc/6gt/uXQr+cNtW4x9HRZ+/7p2LDbgtin1Bin49ij5/AJu3FdvmngW/JkXr0qljXTNFq/XPwKJf36LfH1D8a9yp4Cr6L4XWEkVff7X+GQgw/cwbC433udvPKzReRzN5yJAaThW0zNo3Z1T1HbDnLiNr8lz5545VlaQ+kp4imxulppIpZmZmVrl3wpdFMzNrL6ryozZ5Ulqrqoh4BRiY3yZpL6Cx5MpJEfHyzqiXmZmZmZmZWVs4oWI7XUqa1LV3PczMzMzMzKztVMO9SKrJQ35aSdIoSQfl1q+SNKw961QpSZcUGGurpGWSfitpesPdhCp8bp2kTxZVl5LYV0q6oPmSZmZmZmZm9m4laU9JcyQ9nf7fo0y5PpJ+Jmm1pFWSPtpcbCdUWm8U8FZCJSIuj4j72686LdJoQkWZll4Tr6dbNB8CbAYqmplKUheyXipVSaiYmZmZmZnZziF1quqjjS4GHoiI/cmmnri4TLn/AGZFxIHA4cCq5gI7oZJI6p+yUDdLekLSbEk9JI2TtFjSckl3Seop6VhgJNlth5dJGiBpkqTTJA2XdGcu7lBJM9PyyZIekbQ09eboVaYuF0lamY45IW2rk7RQ0gpJ9zRk1STNlTQoLfeVtCYtj5V0t6RZKRN3ddo+AeiR6j0l1+4bgKXAZZKuy9VlnKRrKzyNDwP7pQzgz1NdF0o6LMW6UtJESbOByWS3ah6d6jK6tFdJ6vXSPy1fljKFcyRNbSjX2OtTYV3NzMzMzMzs3e/TwG1p+TayzhFvI2k34ATgJwARsTnNB9okJ1Tebn/g+og4GHgFOBW4OyIGR0RDhurciFgAzAAuTL0znsnFmAMcI2nXtD4amCapL3ApMCwijgSWAN8srYCk4WQv8NHpmFenXZOBb0XEYcBK4IoK2lOXjn8oWeKiX0RczPZeJWNSuQOAyRFxBHANMFJS17TvbODW5g6UepwMT3X7DvB4quslqe4NjgI+HRFnAJcD01JdpjURexDZa3EE8FlgUG73Dq9PBXUdL2mJpCVPz5zZXHEzMzMzMzNrUk3f5WfviHgBIP3/3kbKfBh4CbhV0uOSbsl9py/LCZW3ezYilqXlx4D+wCGSHpa0EhgDHNxUgIioB2YBI1KS4VPAL4BjyIYIzZe0DDgL2LeREMOAWyPitRRvraTdgT4RMS+VuY0se9acByJifUS8ATxZ5ngAz0XEwnS8TcCDwCmSDgS6RsTKJo7RI7VnCfC/ZBm9jwG3p3gPAnulNgDMiIjXK6h73seAX0TE6xGxAchnQVr0+qQ6TYyIQRExaP8RI1pYFTMzMzMzM9uZ8n8UT4/xJfvvTyMcSh+frvAQXYAjgf9MHQ02UX5o0NueZNu9mVveCvQAJgGjImK5pLHA0AriTAO+BKwFFkfEBkkC5kTE6fmCko4Gbkqrl5Ol36IFda5ne2Kse8m+0vaUe703lazfQtazZDXN9055PSLq8htSW0s1tKn0WHn5tsD29jSVkpxEy18fMzMzMzMzK0i17/ITEROBiU3sL3uDGEl/kbRPRLwgaR/gxUaK/QH4Q0QsSus/o4KEinuoNK838EIaAjMmt31D2teYuWTZrXFkyRWAhcBxkvYDSHOxDIyIRWnIS11EzABmA+c0zAUiac+IWA+sk3R8inUm0NBbZQ3ZMBqA0yps05bckJ4dpIuoH3AGMLXCmHkPkc6VpKHAXyPi1UbKlZ7DNWTnDUlHAh9K239D1uOne5p35lO555R7fczMzMzMzMxmkI0QIf3/i9ICEfFn4HlJB6RNJ5GN8miSEyrNuwxYRDY3yurc9juAC9P4qgH5J0TEVuBesjlF7k3bXgLGAlMlrSBLsBxYerCImEX2gi9JQ2kaJmk9i2wS3BVkc6NclbZfA5wvaQHQt8I2TQRWSJrSRJk7gfkRsa7CmHlXAoNSXSew/eIt9WvgoIZJaYG7gD1Tu88HngKIiMVk52Q5cDfZ8KL1KUa518fMzMzMzMx2ipqeQ2UC8AlJTwOfSOtIer+kX+XKfQWYkvvO/b3mAiuiJaNLrKOQdC9wXUQ80N51AZDUKyI2pp47DwHjI2JpW+P+w7x5hb4B+nTbVmQ4Nm4pNudZX/DbvUvBPfu21fjHUdHnr3vnYgNui2JfkKJfj6LPH8DmbcW2uWfBr0nRunTqWNdM0Wr9M7Do17fo90c1Xt9OBZ9D/6XQWqLo66/WPwMBpp95Y6HxPnf7eYXG62gmDxlS3XEyO9H6zfdV9R2we7e/q8lz5TlU7G0k9QEeBZbXSjIlmSjpILJ5VW4rIpkCxf/i9erm2v5VrujavRN+cShS0V+e6ov+slNotOKvl6LPH0CXghMgtf6Fu+hrxmpL0a9v4T+RqnD51fZPTXu362i/x0DxCRAnaKyB1DE/0Z1QsbdJ99oemN8maS+gseTKSRHx8k6q1xk74zhmZmZmZmZmlXBCxZqVkiZ17V0PMzMzMzMzq0Udsxdtx+yXY2ZmZmZmZmbWBk6otJCkUWkuj4b1qySVved1LZF0SYGxvi3pCUkr0l16jm5FjDpJn8ytj5X044Lqd6WkC5ovaWZmZmZmZm2hKv+rVU6otNwo4K2ESkRcHhH3t191WqTRhIoyFV8Lkj4KnAIcGRGHAcOA51tRnzrgk80VMjMzMzMzM6s1HT6hIqm/pFWSbk49LmZL6iFpnKTFkpZLuktST0nHAiOB76deGQMkTZJ0mqThku7MxR0qaWZaPlnSI5KWSpouqVeZulwkaWU6ZsO9seskLUw9Qe6RtEfaPlfSoLTcV9KatDxW0t2SZkl6WtLVafsEoEeq95Rcu28AlgKXSbouV5dxkq4tc9r2Af4aEW8CRMRfI+JP6XmDJS1IbXhUUm9J3SXdmtr2uKQTJXUDrgJGpzqNLjkXIyQtSuXvl7R32n6lpJ+m9v9e0ldzz/m2pN9Juh84oInXfLykJZKWPDVzZrliZmZmZmZmVgH3UOnY9geuj4iDgVeAU4G7I2JwRBwOrALOjYgFwAzgwoioi4hncjHmAMdI2jWtjwamSeoLXAoMi4gjgSXAN0srIGk4We+Xo9Mxr067JgPfSj1BVgJXVNCeunT8Q8kSFv0i4mLg9VTvMancAcDkiDgCuAYYKalr2nc2cGuZ+LOBfpKeknSDpCGpDd2AacDXUhuGAa8DXwKIiEOB04HbyK69y4FpqU7TSo7xG+CYVLc7gIty+w4E/g74CHCFpK6SjgK+ABwBfBYYXO7kRMTEiBgUEYMGjhhRrpiZmZmZmZlZWb7LT+bZiFiWlh8D+gOHSPpXoA/QC7ivqQARUS9pFjBC0s+AT5ElAYaQDRGaLwmgG/BIIyGGAbdGxGsp3lpJuwN9ImJeKnMbML2C9jwQEesBJD0J7EvjQ3Kei4iF6XibJD0InCJpFdA1IlaWaevGlMA4HjiRLHF0Mdm5eyEiFqdyr6Y6fAz4Udq2WtJzlNyauRF/k+LuQ3bOns3t+2XqHfOmpBeBvVNd7mk4f5JmNBPfzMzMzMzMCtEx+2o4oZJ5M7e8FegBTAJGRcRySWOBoRXEmUbWG2MtsDgiNijLosyJiNPzBdMkrjel1cvJ7jMVLahzPduv2u4l+0rbU+513lSyfgvZPCurKd87BYCI2ArMBeZKWgmcRTZ0qLE2tKaP1o+AayNihqShwJW5feXa15LzZ2ZmZmZmZgVInQc6nI6ZRqpMb+CFNARmTG77hrSvMXOBI4FxZMkVgIXAcZL2A0hzsQyMiEVpqEtdRMwgG0ZzjqSeqdyeqZfJOknHp1hnAg29VdYAR6Xl0yps05bckJ4dRMQioB9wBjC1XDlJB0jaP7epDniOLBHzfkmDU7nekroAD5HOoaSBwAeB39H0udwd+GNaPqvZlmXH+Iyy+W96Ax7LY2ZmZmZmZlXjhEp5lwGLyOZGWZ3bfgdwYZosdUD+CanXxr3A8PQ/EfESMBaYKmkFWYLlwNKDRcQssvlZlkhaBjTc8vcssklwV5AlLq5K268Bzpe0AOhbYZsmAiskTWmizJ3A/IhY10SZXsBtkp5M9ToIuDIiNpPN3fIjScvJzl134Aagc+rJMg0Ym4bs/Bo4qLFJacl6pEyX9DDw1+YaFhFLU+xlwF3Aw809x8zMzMzMzIqgKj9qkyI8SsK2k3QvcF1EPNDeddkZvjhvXqFvgM3bin2zd+lU7PtzW8Fv96Lb27OzP4/aYlvB8brU7s+uqqkv+BIs+q8Wtf4av7G12IDdiv4MLDQaPPpUse392IHF1rC+4M/oD/XeUmg8gOc2lu042ypF/5yzd7dOBX8G+vpru+ln3lhovM/dfl6h8Yo2eciQd81vW5vqH6rqO2DXLifU5LlyDxUDQFIfSU+R3QmoQyRTzMzMzMzMrO066m2TPSmtARARr1By5x1JewGNJVdOioiXd0a9zMzMzMzMzGqREypWVkqa1LV3PczMzMzMzKyWdczBLx2z1e1E0ihJB+XWr5I0rD3rVClJlxQY69uSnpC0Ik1Ie3Qr4wyVdGxufZKkSu94ZGZmZmZmZtZq7qGyc40iu/vPkwARcXm71qZlLgG+V7pR2Q3HFREVzaQn6aPAKcCREfGmpL5At1bWaSiwEVjQyuebmZmZmZlZG9XyPCfV5B4qbSCpv6RVkm5OPS5mS+ohaZykxZKWS7pLUs/Uk2Ik2S2Ql0ka0NCjQtJwSXfm4g6VNDMtnyzpEUlLJU2X1KtMXS6StDIdc0LaVidpYeoJco+kPdL2uZIGpeW+ktak5bGS7pY0S9LTkq5O2ycAPVK9p+TafQOwFLhM0nW5uoyTdG2Z07YP8Nd022Qi4q8R8af0vJPS7ahXSvqppF3S9jUp8YKkQan+/YHzgG+keh2f4p8gaYGk35frrSJpvKQlkpY8NXNm+RfYzMzMzMzMrAwnVNpuf+D6iDgYeAU4Fbg7IgZHxOHAKuDciFgAzAAujIi6iHgmF2MOcIykXdP6aGBaSiJcCgyLiCOBJcA3SysgaThZ75ej0zGvTrsmA9+KiMOAlcAVFbSnLh3/UGC0pH4RcTHZ3X/qImJMKncAMDkijgCuAUZKarj34dnArWXizwb6SXpK0g2ShqQ2dAcmAaMj4lCy3lPnl6tkRKwBbiS7xXNdRDycdu0DfIysF8yEMs+dGBGDImLQwBEjmjkdZmZmZmZm1hRJVX3UKidU2u7ZiFiWlh8D+gOHSHpY0kpgDHBwUwEioh6YBYyQ1AX4FPAL4BjgIGC+pGXAWcC+jYQYBtwaEa+leGsl7Q70iYh5qcxtwAkVtOeBiFgfEW+QDU1q7HgAz0XEwnS8TcCDwCmSDgS6RsTKMm3dCBwFjAdeIkscjSVL0DwbEU+1sL6lfh4R2yLiSWDvVjzfzMzMzMzMrFmeQ6Xt3swtbwV6kPW0GBURy1OyYGgFcaYBXwLWAosjYkOan2RORJyeL5gmcb0prV4OCIgW1Lme7cm07iX7SttT7hrZVLJ+C9k8K6sp3zsFgIjYCswF5qak01nAslbWt1S+/rWbyjQzMzMzM3vX6JhfvdxDpTp6Ay+kITBjcts3pH2NmQscCYwjS64ALASOk7QfQJqLZWBELErDXOoiYgbZMJpzJPVM5faMiPXAutzcImcCDb1V1pD1EgGo9K44W3JDenYQEYuAfsAZwNRy5SQdIGn/3KY64DmyREz/hrY2Ud9Tc89t6nyamZmZmZmZVY0TKtVxGbCIbG6U1bntdwAXpolXB+SfkHpt3AsMT/8TES8BY4GpklaQJVgOLD1YRMwim59lSRoadEHadRbZJLgryBIXV6Xt1wDnS1oA9K2wTROBFZKmNFHmTmB+RKxrokwv4DZJT6Z6HQRcmYYYnQ1MT71WtpHNkQLwHeA/JD1M1mumwUzgMyWT0pqZmZmZmdlOJDpV9VGrFNGSkSJm5Um6l2yS2Afauy6V+uK8eYW+ASq6d3QLFP3RUXT9ila7H5WZTgX3ZNzWwT5+iz5/UPw5rK/x16RLjfemrfXPwKKtebXYkdP9d6svNF7R+nQr/qfIq1uKfZU72ueqmTVt+pk3Nl+oBT53+3mFxps8ZEiN/2Sv3JtbH63qJ/AunT9Sk+eq1n9XsXcASX0kPUV2J6B3TDLFzMzMzMzMiqAqP2qTJ6W1NouIV4CB+W2S9gIaS66cFBEv74x6mZmZmZmZmVWLEypWFSlpUtfe9TAzMzMzM7Pqym5Q2/F4yE8rSBol6aDc+lWShrVnnSol6ZICYuyVJoJdJunPkv6YWx8o6bctjHeepC+m5UmSKr3zUHNx50oaVEQsMzMzMzMzszz3UGmdUWR34nkSICIub9fatMwlwPdKNypLKSoimp1xLt/7RNKVwMaIuCat929phSKi2NmizMzMzMzMbCdyD5UOS1J/Sask3SzpCUmzJfWQNE7SYknLJd0lqaekY4GRZLcjXiZpQEOvCknDJd2ZiztU0sy0fLKkRyQtlTRdUq8ydblI0sp0zAlpW52khZJWSLpH0h5p+1s9MCT1lbQmLY+VdLekWZKelnR12j4B6JHqPSXX7huApcBlkq7L1WWcpGtbcUo7l57LXLy3nc+0/UpJF5QGkXR5Kv9bSRNT0qeh3f8u6VFJTzXcMjm9Znek8zQN6NGKupuZmZmZmVkLdNTbJtduzXa+/YHrI+Jg4BXgVODuiBgcEYcDq4BzI2IBMAO4MCLqIuKZXIw5wDGSdk3ro4FpkvoClwLDIuJIYAnwzdIKSBpO1vvl6HTMq9OuycC3IuIwYCVwRQXtqUvHPxQYLalfRFxMdieeuogYk8odAEyOiCOAa4CRkrqmfWcDt1ZwrFKNnUto5Hw2E+fHqfwhZMmRU3L7ukTER4Cvs/18nA+8ls7T/wGOaiyopPGSlkha8tTMmS1vnZmZmZmZmXV4Tqhs92xELEvLjwH9gUMkPSxpJTAGOLipABFRD8wCRkjqAnwK+AVwDHAQMF/SMuAsYN9GQgwDbo2I11K8tZJ2B/pExLxU5jbghAra80BErI+IN8iGJjV2PIDnImJhOt4m4EHgFEkHAl0jYmUFxyrV2LmEFp5P4ERJi1L5j5eUv7uR+CcA/5XasgJY0VjQiJgYEYMiYtDAESMqbpSZmZmZmZk1xrdN7ujezC1vJesRMQkYFRHLJY0FhlYQZxrwJWAtsDgiNqShKnMi4vR8QUlHAzel1cvJrpRoQZ3r2Z4U616yr7Q95V7rTSXrt5DNs7Ka1vVOaezYDUNvJlHh+ZTUHbgBGBQRz6e5WvJtbDhGadtacv7MzMzMzMzMWsU9VJrWG3ghDYEZk9u+Ie1rzFzgSGAcWXIFYCFwnKT9ANJcLAMjYlEaflMXETOA2cA5ublF9oyI9cC6hnlCgDOBht4qa9g+rKXSO+NsyQ3p2UFELAL6AWcAUyuMWaly57MxDcmTv6b5Zipp30MNcSUdAhzW2oqamZmZmZlZZVTlf7XKCZWmXQYsIpsbZXVu+x3AhZIelzQg/4SI2Ep2B6Dh6X8i4iVgLDBV0gqyBMuBpQeLiFlk87MsSUODGiZqPYtsEtwVZHOjXJW2XwOcL2kB0LfCNk0EVkia0kSZO4H5EbGuwpiVKnc+dxARrwA3k80Z83NgcQXx/xPolc7TRcCjbairmZmZmZmZWVmK8AgJeztJ9wLXRcQD7V2Xajvn4bk1/QbYvK3YbGyXgpO79QWfvY6W4W32HuUtVPTru63g17fo9kLxdexU8DnsaNf0GwV/ZnXvVNMf0Sx9tthX+CMDthYar77g1+MDu9YXGg/gj5s8+tzaT9Gf+UX/TLLaM/3MGwuN9/r/Tq3drhcttDVWVPUd0FmH1eS56mi/61kTJPWR9BTZnYDe9ckUMzMzMzMzs9bynwXsLWmYzcD8Nkl7AY0lV06KiJd3Rr3MzMzMzMyslnXMvhpOqFiTUtKkrr3rYWZmZmZmZlZLOmYaqQCSRkk6KLd+laRh7VmnSkm6pKA4cyX9Xcm2r0u6QdJISRc38/yhko4toi5mZmZmZmbWPnyXH2upUcBbCZWIuDwi7m+/6rRIowkVZVpyTUwFvlCy7QvA1IiYERETmnn+UMAJFTMzMzMzM3vHcUIlkdRf0ipJN0t6QtJsST0kjZO0WNJySXdJ6pl6VYwku5XxMkkDJE2SdJqk4ZLuzMUdKmlmWj5Z0iOSlkqaLqlXmbpcJGllOuaEtK1O0kJJKyTdI2mPtH2upEFpua+kNWl5rKS7Jc2S9LSkq9P2CUCPVO8puXbfACwFLpN0Xa4u4yRdW+a0/Qw4RdIuDecQeD/wm3T8H6ft70nnbnF6HJfKngd8I9Xl+HQOfyhpgaTfSzotPb+XpAfSeVsp6dO512y1pFsk/Ta1Z5ik+anNH2nZVWBmZmZmZmYtpyo/apMTKm+3P3B9RBwMvAKcCtwdEYMj4nBgFXBuRCwAZgAXRkRdRDyTizEHOEbSrml9NDBNUl/gUmBYRBwJLAG+WVoBScPJer8cnY55ddo1GfhWRBwGrASuqKA9den4hwKjJfWLiIvJ7uJTFxFjUrkDgMkRcQRwDTBSUte072zg1saCp/lVHgX+Pm36AjAtdrwX93+Q3YZ5MNk5vSUi1gA3pu11EfFwKrsP8DHgFKChh8sbwGfSeTsR+IGkhnfVfin+YcCBwBnp+RdQvifOeElLJC353YyZjRUxMzMzMzMza5InpX27ZyNiWVp+DOgPHCLpX4E+QC/gvqYCRES9pFnACEk/Az4FXAQMIRsiND/lAroBjzQSYhhwa0S8luKtlbQ70Cci5qUytwHTK2jPAxGxHkDSk8C+wPONlHsuIham422S9CBZz5NVQNeIWNnEMRqG/fwi/X9OmTYdtD0Hwm6SepeJ9/OI2AY8KWnvtE3A9ySdAGwDPgA07Hu2oX6SnkhtDkkryV6/HUTERGAiwDkPz63q/dLNzMzMzMze7XLf9ToUJ1Te7s3c8lagBzAJGBURyyWNJZv3oznTgC8Ba4HFEbEh9aiYExGn5wtKOhq4Ka1eTpY8aMmX/Hq29zTqXrKvtD3lXu9NJeu3kPXuWE2Z3ik5PweulXQk0CMiljZSphPw0Yh4Pb+xzJsuX+eGAmOA9wBHRcSWNKypeyPlt+XWt+Hr28zMzMzMzKrEQ36a1xt4IQ2BGZPbviHta8xc4EhgHFlyBWAhcJyk/QDSXCwDI2JRGvJSFxEzgNnAOZJ6pnJ7pl4m6yQdn2KdCTT0VlkDHJWWT6uwTVtyQ3p2EBGLgH5kw2emNhUoIjam9v60ibKzgS83rEiqS4tNncO83YEXUzLlRLKeNmZmZmZmZlYTOlX5UZtqt2a14zJgEdncKKtz2+8ALpT0uKQB+SdExFbgXmB4+p+IeAkYC0yVtIIswXJg6cEiYhbZ/CxLJC0jmwsE4CyySXBXkM2NclXafg1wvqQFQN8K2zQRWCFpShNl7gTmR8S6CuJNBQ4nOyeN+SowKE2o+yTZZLQAM4HPNExK20T8Ken5S8iSWqubKGtmZmZmZmZWddpx/lAzkHQv2YSxD7R3Xaqp1udQ2byt2LGIXQoe2lhf8NnraBnebQXHK/r13Vbw61t0e6H4OnYq+Bx2tGv6jYI/s7p3qumPaJY+W+wr/JEBWwuNV1/w6/GBXesLjQfwx00enWvtp+jP/KJ/JlntmX7mjYXGe/1/p76LJh55qsrvgIG1ea4iwg8/3nqQTb77FDC9vetSSw9gvOO9e+O9E+roeI7neO/sOjqe472b4r0T6uh4jvduiudH7T462h/PrBkR8UpEDIyIzzVsk7RXGpZT+tirPeu6k413vHd1vGrEdDzHc7ydF68aMR3P8Rxv58Z0PMdzPHvHcT9La1ZEvEw2b4uZmZmZmZmZ0fGGd5uZmZmZmZmZtZkTKmaVmeh47+p41YjpeI7neDsvXjViOp7jOd7Ojel4jud49o7ju/yYmZmZmZmZmbWQe6iYmZmZmZmZmbWQEypmZmZmZmZmZi3khIqZmZmZmZmZWQs5oWLWDEm7tncdypHUWdL7JX2w4dGGWLs0sm3PttWwOJK+LGmP9q5HcyT1kHRAG2Ps2dSjqLrWMkl7SDqsjTE+V8m2Fsb8mKSz0/J7JH2oLfFqmaR/r2RbK+LuK2lYWu4hqXdbY9ai9Pl8f8Exz21k24RWxpKkfm2v1Q5xO8x7JE/Sewv6WbyrpE659U6SehZTy9oj6ZCC4711/iQNlDRSUtc2xtxb0inp8d5iampm7xaelNasDEnHArcAvSLig5IOB/4pIv65lfEGAd8G9gW6AAIiIlr1pVHSV4ArgL8A29LmtsT7JTAqIrak9X2AeyPiqFbG+xIwJSJeSet7AKdHxA2tjPevwBeApcBPgfuijR9gkgSMAT4cEVelX4LfFxGPtjLeCOAaoFtEfEhSHXBVRIxsYZxngSC7Rj4IrEvLfYD/jYgWfUGRtDLF22EXbbtmftjI5vXAkoj4RSvizQVGkr0/lgEvAfMi4putrN/SiDiyuW0tiHcFMAg4ICIGSno/MD0ijmtlvBnAHcAvImJTa2KUxLuL7L3xPxGxrbnyFcRr7PytaO31kp4/DhgP7BkRAyTtD9wYESe1ME65axqANlzTRX8mzADOjIj1rXl+I/H+B/iviJiS1m8AdomIHRItFcZ7rLWf8WXiFf0e6Qn8C/DBiBiXrpcDIuLeNtTxPcC3gIOA7g3bI+LjrYw3EvgB8H7gRbKf8asi4uBWxlsIDIuIjWm9FzA7Io5tYZyPR8SDkj7b2P6IuLuF8WbS9HuuRT/ncnF/A3QDJgH/3fA7Q2tJegw4HtgDWAgsAV6LiDGtjPd54PvAXLKfmccDF0bEz9pQxz2A/Xn79fdQK2N9sbHtETG5hXGa/DkbEde2Z7yS2J8F/h14L9lr0vC7zG4tjNPk7wIRsbSV9Sv0932rfV3auwJmNew64O+AGQARsVzSCW2INwW4EFjJ9gRIW3yN7BfLlwuIBfBzYLqkU4F+ZO2+oA3xxkXE9Q0rEbEufZlqVUIlIi6VdBlwMnA28GNJdwI/iYhnWlnHG8hei48DVwEbgLuAwa2MdyXwEbJfvIiIZZL6tzRIQ8JE0o3AjIj4VVofDgxrRb1OacVzKtEdOBCYntZPBZ4AzpV0YkR8vYXxdo+IVyX9I3BrRFwhaUVLK5XO0yeBD5QkfXYD6lsaL+czwBFkST0i4k9t7F3xA2A08G+SHgWmkSUx32hlvP8ke2/8UNJ0YFJErG5pEEnnA/8MDCg5/72B+a2sW4Mvkb1HFgFExNOt/ItvwzX9pfT/7en/McBrbahf0Z8JbwArJc0B3kqaRcRXWxnvs8AMSduA4cDa1ib5k4WSBkfE4jbEyCv6PXIr8Bjw0bT+B7LPm1YnVMh+Fk8DPgWcB5xFlrxtre8CxwD3R8QRkk4ETm9DvO4NyRSAiNjYyh4qQ4AHgRGN7AugRQkVsj8WQHYNvg/4r7R+OrCmFfXLKhLxsZQoOwdYkj4Lb42IOa0MqYh4LfXm+lFEXC3p8dbWj+yL8eCIeBHeSsjdD7QqoZJ+vn0N+BuyPxwcAzxC9pnTGvnPpu7ASWTvvxYlVMg+34vUEO8AsjrOSOsjgFYlj3KuBkZExKo2xvlB+r87WSJ4OVny4zCyn1Efa2Xcon/ftxrnhIpZEyLi+ewPlm/Z2oZwL0XEjOaLVex5st4AhYiImyV1I0us9CfrjbOgDSE7SVJDLxJJncn+CtWWOoakPwN/JvtivAfwM0lzIuKiVoQ8OiKObPhlKyV92lLH+ohYX3LNtMXgiDivYSUi/kfSd1saJCKeK6pCJfYDPh4R9QCS/hOYDXyC7BeJluqSekZ9nuyX2Nb6E9lfJUeSfRlrsAH4Rhvibk7XYMM13abhgBExD5iX3hsfB8aR9TBp0V/ZcvHuB+6XtDvZl5w5kp4Hbibr1bClwlD/DfwP8G/AxbntGyJibWvqlvNmRGxueI9I6kITf/Uup+GalnRcSe+HiyXNJ0uGtEbRnwm/TI820duH+v0j2ef0fOAqSXu24XU5EThP0hqyhE9b/5Ja6HsEGBARoyWdTlax19X2D9i9IuInkr6Wew/Oa0O8LRHxsrKhOZ0i4tdq29C4TZKObPjruKSjgNdbGiQirkiLV0XEs/l9asUwrHSukPTdiMj/cWmmpDZ9QU6J1UvJPrd/CByRXudLWtqTJquiPkqWXG3oudWW7zudGpIpycu0bcqEr5ElGBZGxImSDgS+09pgEfGV/Hr6/L+9TPGm4rS6Dk3FkzQbODIiNqT1K9n+R5jW+ksByRQi4sRUpzuA8RGxMq0fQtv+oFj07/tW45xQMSvveWXDfiL9Qv1VoC0f4FdIugV4AHizYWMrfllo8HtgrrKhOvl4bemWKbLeKcuAYyQd04ZumfcBd6ZeFkH2l8BZrYyFpK+S/SXxr2RDsS6MiC3Kxko/DbQmobIlfZlt+OX/PbTtrwm/lXQG0Dn9xe2rQFuSUn9Nv2T+V6rjP5D9Mtcqko4BfgT8LVlyqzOwqaXdZHM+AOzK9sTersD7I2KrpDfLP62s75BdN7+JiMWSPkz22rZIRCwHlku6h6x9W+GtpN4OcwW1wJ2SbgL6pN5W55AlK1pNUg+yv9iN/v/bO/N468rx/78/zSmlKPoiDRJpVmhAZYxSoQhFMoYKv8yUZIpMGVKSJFSSypeKNI96mkVfvmUIX3P1aFR9fn9c937OOvvZZ1j3utdzzuO536/XeZ2z1z772vdae++1133d1/X5AJsCx3aM91DifbIHcBWxUrY18dnZZjoxUlJwLrBBD8m48yS9F1hW0rOJSpjTO8RbTtLWti+Eea2aXSbxRc8Jto9Nr/Hqtm/sMK45jLUBDn6/IP0YWCsz7vYdxjSKUZ+Rr3aId286foPXY20a33eZDBKLf5L0AiIB+6gO8W5VtOWcDxwv6S90q4Tbn6gW/WO6vRpxfsjlZOLc0uS7QG6r1yqS1rJ9E8xLzqySOziFVtZexHv5x0TlwZWKdrFLaF9Jsz/wHuAU2z9P3yPn5I4POEPSmcC30+2XEgnnXO62fbckJC1t+5fqqLs2xJ1EO1EWkpYhElFPZHxL0msyQ64O3Nu4fS+xaJcztkH72hWSTiASyyWupx8/SKakONcrWrZzKX29X5nl1IRKpTIxbwQ+R0wabyFW3t886SMmZy+iPWJJGpontL9YGPC79LMU3So/hss8T5lge1veBbwBeBNx8X8W3S6sHwa8aHiCZ/sBSbktLZ8n9ndVSR8BXgK8v8MY30pUVtxDXHydSZSD57I7oZNzCvFeOZ9upeRfIHRoTiLKW/ckqkxyORS4WqF9IuDpwEfTqnQrMc40iX10c2U8XbC/uMP4ziJapAbl88umba20CBrj+VRKAtxOlDF/sENZOumC8ClEovGLwLnuoH0i6XvEOeY4YlLyp3TXCZKuaBMrfa6ukbS67d/ljmkE7yYu1q8jzg8/pNt5YW/ga2lVFuBWYhKfS9Fzghq6SsCaytRVckvdpBZxfytpa2Ad28ekBNLyHeIV/YwQ578zgEdLOh7YCnh1h3gAh6T3yzuIBPMKdKtc24lo7XobURWxIvkVUqRk8uOJ4yfgly2qy+aRYjwRWFHjdVRWoDFRzuBtxGLOTen2GsRnOZcvEOeA99qeV4mT2sVaf/YaVUcPlrR8+h7JbbHD9gHp+G1NvB5H2j5liodNxi2SHkIkA34s6Z9EUi8Ljde2WYzQBjqxw/iOA35JtLwfTLynuywmHgdcnhY4TLQFtm1HGtBsX7uTaAEf0OV6+hcpAdJcvOqyz6Wv9yuznCpKW6ksICRdZ3uDmR7HVEhazgUEMvtCobfQXDXpNNlLF53PJC6Uzi5RRpriLg4sZ/v2ArGWb/bUd4hzhe3N1BAWlXSxW4odDsVcjdDEEHC57S4XhucMSnBLIOlq2xtPta1FvDWBPzlpnKSV84fb/k1GrMWA9wIfG1TQdEXS8530dhrblradtaIv6adEafrljNf/yBKfTDF3AX6YO6ZJ4q5AXNNktUFKWnPQFlHynKAQyNyOSJZtkrZlfxeovNh3aRHZT9h+11TbWsZ8KKEzIaJN4m+5sWYzKi8iuxOwM9H62Gw/mAt8xx1aehWugI9PN39Z+vPcBUkbEBP2lYn3zF+BPW3/PDNesfP+iNjPIBJwZ9i+d6r/nyTGgPuA39q+pcOYrnJoAV1re0OFQ9KZzhRtTjGfxJgeyfm2u2jaDFo9L5pqW4t4yxCLf4NWtvOBLztTz2xhud6vlKMmVCqVCVB5B5OjgM/YvqHjuD5re39NoLifO9lR9BwfTTlXo60IkdZhlfOs0vS00vtpCjkppJhrA7fYvkfSNoQQ2Tec6TIg6VtEZdP9RIn+isCnbX8yM15pp6nziYqNrxI6NH8CXm17o5x4KeYjGXuNgU5uBR8hjtkJjJ/A5yrtXwS81eO1CL5ge4vJHzlhvCuALQcXvopWwItsZwmWSrokdywTxCvtavSMUdvTCnAWko4hEgznEw5HZzpp8LSMU9qRYo7tJ0k62y0dh6aIe5ntpwwmKWlbtlPSBEnCebFz4pFEZAuNr4gzlHpy30ixVyH0itZg/HmrVWWTpAsdgqpzmb8dy27vOPIhhxD3MSPudtvxNeJuYfuSnMdOEnNL5j9+bV1l+nKfuxh4n+1z0u1tgI/mLhz0cN4faamdsziUFm7OtJ0jVj9RzMttPzldL+xDXCtcnnvt1hjnwxn/fsleDCv9XVeaUtf7lYWH2vJTqUxMaQeTrYFXKSxx7yH/omEgNvapSf+rPZ+lrKvR0URp8By6ifkOOISyTgoQveWbSXoskWQ4nRDkfH5mvPUcLjWvIFoZ3kXsf1ZChfJOU3sQuilvIV6bR9OhpUYhvPhS4nPRLGvNFSgcXPA2y+VNvvvB/pTVIliiuYroEFft0m53lsJV63vusLoh6RFEa+KykjYhzi0Qpf057iBAJE4kPZwxF4nLPV6cMSfmXmnFc3vg5cCXFKLSr20ZqrQjxWKpWuNxo5I1bRM0DUrrKpUW+y4iIqsxZ6i1VMYZ6rBJ7utyTgA4FbiAaEvM/m6yvXX6XeS96CQia3uvEvEkvdP2ocDLlUR9h54vqw1G0nHA2oTW2uD4mfZtHH25zy03SKYA2D43932dKH3e/2/GEm/LAGsCNxLtWa1w6JXdKWlFF7JmB45MlW8fIK49lgc+mBtM0luJ1r0/E++XQeKxdcIsLfxtSej4NM/TKxDXNrljHF4ABKBDEqnU9X5lIaEmVCqViSntYPK8EoOyPSf97uJKMFHskq5Gt9nuItw2TGknBYAHbN+XSqw/Z/twdbNXXDJNFncmKiH+PZio5FLyNfGY/sxddHAVaLAz0SpQpNzbBdt9UrwiWgQN/irphU7q/YqS+i7tB28nBFTvl3QXmSvbRNLt1YSwZnPiP5doK8pC0m5EMvDcNLbDJR1gO8sudED6XPyIuKheltCgaJVQcWFHCkJbaGfiuqhksqa0rlJRsW/KCS0XdYYqfS4Y4kHu0II0ilRRszXxmlzojJaG0lVXjGlAtNJPmgabEYsHXb/b+nKfu0nSBxhbfHolcPMk/z8VRc/7HmoFSe+dLho0Ra3ZbQ80rc4jX+y6yX7EdUK2oH6DpYgEz/B5+nZC7yqX0guARa73KwsPNaFSqUxMaQeTov11abXzY4QAWVNTJPcLsLSr0TmSPkmIcDVVznNLtQdOChdQxkkBwtFjd0KcdSB2tmSHeF8BfgNcA5wv6THEF30uRV4TSSfa3m2iEusOqyY3EcerSEIlVUN8lPicbS9pPWAL20dnxluGWDUfTHQukHSEM/uiicnr8ZK+QCQYfk+8d7IouLJ9LHCspBfbPrlEzMT7COvuv8C8VomfEA4hWUh6HpG82JZI1HyVsMluG+edtg+VdDij39OtJhMOB55PpPaUYolg23cSx7GLDXiTomLfLicia9u/UWi8jEPdbJ1RWJgOf8/liloC/EAj9IZykfRBYFfGBCe/Lukk24e0DFW06sr26el3J+ewEVwPPIJoGe2MyrvPvYZYMPge8Rk5nxAJzaXoeX8Yh6NRVvtQoog1+4AJEnu3AXNsX50R8veMXUd3wmOCw18vnJArugA4GJuGNP8q/7lUDZVKZQIk7U24O5wLYw4mxCrjQbYPaBlvMJkdV+bpTA0QSRcSZZSfIZIBexGf6QMz4z2McDV6FmMX6vvlripIGmVTaGcKm6WS3bvT2AZOCsd3WfVIE/Y3ApfY/rZCfO6ltj+eG3PEcyzhDI2I9Ngir4mk1Wz/KSV45iP3wkTSycBGzG8NmFtK/iPgGKL/fSNJSwBXDa/otYh3IlGl8c20aXdgJdu75sRrxF2e+KzN7Rhn8F5e0/aHJT0aWM325S3jlF7ZHsQdJ6ynENK9Jvf1SDG+Q2in/KhLZZOkHW2fLulVo+5vO4mU9Erb35T0DkYnaNpqsozUuGrEyxb2nY1I+oHtHVKJ++B7boBzE/2pDWsbIqHyQ6JV7ELb2avRCs2T5Yhz1r/JrwwbxPsFsInHi5ZeafsJuWMsSUqEvov5k1K538XnABsTYtXN836uftsVjHCfs10qCVmEguf95vl6McLS+qG2n9shZglr9kGsbxGvw8DO/gXAz0gt8KmNrE28o4lk7X8z/v2S20Y5eA+OOk/nvqc/TiTyiiwASnoh0bZYTPOvMrupFSqVygTYPjpN8PYgLOTOIgRM7wBaJVNSvNJlnsvaPluS0oT4IEkXEEmW1jicE17RYTzD8Uq3b9yh0Ip4MvAPQoitUwmpQzBs38btm4HsZEpaqRxFloVmqdckJVMWB452QfE6or/6tCn/a/o8zPaJkt4D4GjH6lJ+u67HC+6eI+matkEak+23D20njTP3wvBLhPbMdkQbyL8I++S2q5Wl9UQGnCHpTCKJDKE/02lV3/bLOo+KXlbfBxoL2ZbBQww0rl5ErOY3k3q/yQ1aujIxtTt+AliVSCxkJRds75B+l7Z3fgmRtL3Kob/zcLrZbBerDGvwG+K1GFS+LQ38b24wSWsRifSnEpPGS4C3Oex/czieEPp+AbGA8CrC+SaXgzo8diS2fy1pcYfj2TEKYdksUgLpnYQmSXYCqcfzfvP9dx+RaMiuLFQha/YGDwU2dXIWTEnN7xKLinOAVgkV4HfpZym66T01+X+Nv5chtOC6VCw/Jf3erLGti1bThymv+VeZxdSESqUyAZJeS/R+PooQX3sqcWHTRQxvHgXKPO9OK8a/kvQW4A/ERXEWkh4HfJmwA1xf0obAC9uWLU90ETKgw2r5awlhtJ8ypudwsO2v5cRLMQerqcNjzG2batpNL0OI7uW06BRtZ0iPKS5e10Mp+R0Ki9SBQOZT6VYqfJWkp9q+NMV7CnkCmYPJdumJ2FNsb6qk22P7n8oQO3R5PZFB3APShHtr4jN3pO1TcmJpfkeUeXfRrTrgccTF9RqMFxNse57+s6RVSx3LVJqOpA/bbgpJn65wz8jlGMYqE7clVSZ2iHcosKML2cUDqKDzF3CX7Qck3aewxv4LmboOkh5v+5eawEGo7Wp04/x8D/BzhYaFCZ21C3PGmPgWkVjdJd1+GZHUfMqEj5ich6YFov0aLRPZGmwur992ZzrvXS3pUKKVqIuI7CCBtAPdEki9nPd7OF8fRCw0nZviX52qbXNZHWhaOP8beIztu5TR7t7H95OTlmCDizq+p0trNvWh+VeZxdSESqUyMfsRK8WX2t5WIW6Z/cUwQZlnl1Wi/QkHj32JbPh2xIVDLkcRlTdfAbB9bSr9bNsH3tfk8wCirPrvAGnifTGQnVBh/GrEMkQf/Mq5wWyPc6aQ9CnyKjj6EhMsIl6n/jRZ3k4cr7UVlser0E1o7inAnpIG9oyrA78YjHu647T9lfTnl2x3+cwO8+9UOTRIIK3CmFtSaxSaMXsz/8pslt1q4mJCpO8Bouw7Cxd2RGlwEnAEUbXQpZrplcAXJd1JJN0uJqxRf95xfKtIWmtQXZAmOqt0iFe0MhH4c+FkysD56wbGO8DkJlSukPQQ4vtpDlHF1aolrsHbgdcz2kEoZzV6cH6eAzQTjee2Htl4ZPu4xu1vpkWTXAZC3H+S9ALgj8RCUd7gymue7EFcEzXd516UOz4KJZAG5/1RCYGcxHfjsaOuCW4j3k9fcXuNr/ts36bx4vVd9By+BVwq6dR0e0fg24q269Y2wKUqhoZiNq/TFgOeRFQCZpM+G8NjzKouZkzz73zKaf5VZjE1oVKpTMzdtu+WhKSl08rWuh3iFS3ztD2Y3PyLboJrAx5k+/KhL+XWXwCTXYR05BZCD2PAXELsLJsRLUOfVWjTZFsEDvEgMlZTHdoQiwPru6VWzxSUEq/bL/0uanuZqraewZgrz43u5spTWmn/4lTVdAJhdfzPjvE+T0zEVpX0ESJ59P4O8Y4j2hOfS7SZvYIOwtI9VYUdZ3uPqba14D7bX84dzwAnTY6U8BhYc75B0urAz2znWqm/DThX0qBdYw06OnqUqExMlUcQCYsTgO8zXjvge6MeNw12pqzz1z7pzyMknQGsYPvayR4zSazXp99FVqNLV+g1JonnSHo3oTVkIkHV5bx9iKQVgXcQiZAViPdlLl9gfs2TdTrE29n254iE/4cAJO1HtD3lUDqBdC7watu/Sbc3JxK4G03ysMm4mUiqNlsp/ww8jkgctj0XFrVmd+h5/ZCxysQ32h4kD3NakEtVDDWZw5hW033EMd07N5jCNe1BRNXfV4nv4tzELYRz3d3E52yg+ZebnKksBFRR2kplAiSdQiQq9idWrv4JLNnhwrooGi16mL3KodCLeQshOrappJcAe9vevsBYr7Q9ssy6RYxvABsApxL7vRPxhfc/kNdKNFT6vRhxcfgmj9fdaBOvWbGxOHHRdLDtL2TG+2mXVZwJYhYTr+sDhavRGoxvF8hy9JC0NqF7dI+kbYANgW/YvrXD+J7MmMXuDcB3bH9z0gdNHu/xwDOJC8Ozu1QLSLoq9Wtfa3tDhYX3mbnvIUk3AlsOV4XZzk4sD58LFMLD19per2WcweRzX6IN5BTGJwS6uMo8HtiKSKo8FfhLl0m4pKUJQUcI6+4uYrybE0myhxCViSsChw7a2lrEOWaSu51b1ZS+R3Z10l/IZaK2nAFt23NGxC95ntmBeC0GbU5ZbWwaLejbGF52K2pRJF1he7PBeSZtu9j2lpnx5rs+GJzLMuPtQLgBPpqxBNJBTrpLGfGeiBPGKgAAgqBJREFUSyR3Pk+4P24PvDb3PSjp/KE2wHnbJP3cLYVLJT2IcBF7Ttp0JnBIRqXLIN7qo7bb/t2o7dOIN8f2k4beL+fZfkZOvD5ofGcOfi9PLJo8Z8oHVyrUCpVKZUJsD/qXD1Ioiq8InJEbT+V6/QfcRNlVjjcDRwKPl/QHIuNfSqS2S4//gP9lvNDfoBy1SwtBs/T7PkJcsLWFa4NmxcZ9RDl9lzLPq1J58EmMb9HJWj1WYfE6FRK0bMQ7Dlib0CxqtgvkWqSeDGwm6bHA0UQ70beA7KSow4HnckkfBT4NHMuY4GgrUsLnZttfTAmfZ0v6U4eEz2Bl9laF1ez/EeebXIpVhSmEht8LLCtpYCUuolf/yIyQzRVKGC8UblpWhkl6L1GZsgpwI3ApsRL/eodQZheexNh5fyNJ2ZP3UpWJtvcCkLSV7XG6QpK2ahtPY3oidxJaGF2dv64Afs7YSvY41yA6aJn1cJ75LNGicp07rFK6sKCvJtDgajxflhsbhTRPJO0OvJz4Lmq2wTwYyBact/2D9OdtRMUBkvbvEO9MSW8Efgz8jWg9/r/ceEQb4OqDBEVKYDws3XfvxA8bj6LF843AY4HrgC06Xm8M+G/G3jfLkhwpiXaYHIpWDAGkxYI3EUK5EG12X+lQ0XpX+n2npP8i3n/Zn8fS10aV2U+tUKlUFhAKd5EjiInAvAt0zy+uNd14RVc5GjGWAxZzR2vAoZiH2O7SyjAc7xEdL2h6Q9JGwNPSzfNzy9NTrFGryF1Wj+cQE5FzB6t/GrLGbRnv1xQUtFTYj67XZVIyFO/KVG31TkLc8vCOK58rEEKRLyMmZKcAJ3b4DF9NVEWtQSRrTyfaJbISPooWnZOJSpxjCMeaD9o+IjNeH1VhH7P9npzx9ImkXxJJih8Q5fKXuYB480ST97aT2QkqEufRISk6qjqgdUWhJrCvboyvrY312wjnjtuI1pdTula9NGKXPs+cAzzTdrb+0VC8PUdtb5uEG3pNPsSQzk5uy5KkxxBVYUsSLQ0rEvpSv86IsybhWvXuxl1ziaq1YpoTkn5ne2TlxTQe+wFioeX1xLn1bcA7bGe1YUl6PnEt+L/ERHtNYB8iKfA625+dZpwTiGTFBUTVzG9s758zpimeZ1PgDbazWhUnqBj6kO1sh0BJXyXef4P38B7A/bZfmxnvA2lszyQEoQ181fYHMuMVvTaqzH5qQqVSWUAMyh4LxvsF8NyhVY4zbK+XM2mU9L/EquwFRCKgtfjYULxP2H7XVNsyY3duIUpxViQuMgeJqfOIio2siZSi7/t1wKCCZBfCGeXwjFiLAx93QQ0VSZfZfkrz/dEsw82Id5Ht1qvZk8Q7CdjX9p8KxbuMWD1+H3Fxc7Ok622vnxnvZkJr4kTblxQYX9GET2kUdpkT4kydJEkrEZoLTfG/LNFSSW8Gjh9U9aTYu9v+UkaslYk2n0Grz/LANUSb02QtMpPFLDJ5V2gLTYhbOq9IGujE7E84Bg1YAdjFLdseJe1MHKe/tHncNOKuSdiN7gT8Fvio7as7xix9ntmcaPk5j/FVObmOds3vi2WISd6VTjo/mTFnzXllJpD0e9uPznzs54B3274r3X4MMdl+dofxDNoARbQBtm7PaS6GKFonLy9xXTTBcxW55iqFpGuGz1GjtmXGXhpYpktCvfS1UWX2U1t+KpUFx+mS9qFcr/87gAtTImTeKkeqMMlZeVqPcEV5GvAphY7ANR5rfWrLs4Hh5Mn2I7blUKKFCMIh6HrG2nz2IFb2cx0G9iascO+ASCARVtutEyoOm+PSFzBFxOvUn6Dlw4AbJF0+FC9r9Z1oiXgj8JGUTFmT/PacxYlV8pF24Jn8O5W970k4KUCsurUdWy9W5YOESarMcYmqNZW3o3+d7S8Objisp18HtE6opHPxDxTip08iEq1vAF5DnBdyuJ5wn+g0eW+bMJkGSxEJoyUY3zZ5O3nOWr24JKXP7alE68EeREvr1TmxGlU+D6bseeYjRHXTMsRx7YTttzZvp8T/cRP8+7TDdnw8msDVbd4TtEzMa34L9Xl3Ub49oksr1n6SlpW0ru0bHe5a2cmUxDqE+PoywIbKawOc195i+z6pzGWRCjlSqr+WM4D7Ja1t+3/Tc61Fhstb41pm1H1drmVKXxtVZjk1oVKpLDgG5bedev3nPdD+YZoUj1rl+GxGyPuJL+iBReqfibLeVkh6E1G+upakQbuLiIv3iyZ8YDuOKhRnbdsvbtz+UGrDyEWM/1K/n27Jn6tVUEMFeCtRrXEPob1zJrGy2pYdG3/fyZgYHsR7Ond8B2U+biSpymrfxu2bgY9nxro/tXOVpFTCpxerckmbEYmEB6fbtwGvyW1xShS1owcWk6RBBUhKfLWe1Ep6IVGxsRWhFfBzIinwDjo4ZlAoSZjO9e8lxNE/TZwDn0a0DeztMReOaeExO9ljgH/EJt8xxcMmizdwSVqDsSqfbJekNEF6GVGZ8nui7ecjOSv5DT7V4bGTsbL7Fa+8k24uOqUo7eo2rfOVpJU8DUe1KRI0y7YcXjNuae2xA4FtiEWsHxILTRfSXsNnI43XoxroU3VNSJVypGx1TmrJAYQb1k3E/j6GPE2pHSe5r8u1zAqUvTaqzHJqy0+l8h+ApCOdLCE7xLiTEDb7NPATz28pPN04KwIrMaIvukM1Dipvt4qkS4ADbF+Ybm8FfMr2Fpnx3k4kzk4hvuR3Ar4+3Z7oEfGKaqgsaqTX8yDmd9/ISmJKOoyY2JRKcDVjb+qOziWlSQnRN9u+IN3emtBKyGoRSzF+ZnvzlLh8isOB6WrbG2fG+yShQXMEccH6RuD3tt/RMs73SFUVwBzb0xaHnCLuyFadjBadwYRrYHm7P6G58zTC0eMpGWN7E/AexhJy/wI+kdMuNRS3s0uSpAeAawn9ntsZmijnVF0pxKkf7vlFeJ8O/GGw2p0R9+PAT22flfP4EfGaejmLA08g2gzfPfGjRsZpJhgeREzwoEAFSGp7Wcf2TxTOcUuUqGCb4LmKtptMN0HT+P/S2mPXEZbLV9neSNLDiRaiySb32WTs7662T5pq20zFa8RYmqjyGSwoFrFqL4EmEPse3lb5z6EmVCqVGaBEAmQoXglb4p2ArYEnE0rzFxNaKmd3iNkUaL3A9jUdYg3brS5OuCq0slsdirkx0R61IvGl/A/gVe4mJLspcRwh9vmq3FilUXmr7UOBQwiF/DOIi8T9nWkjLOmpRHvUE4jVwMWBO3Iv/BVCo29jfiHo3GRhbwmuQp/hY4H9PF5P5LDc8WlEH/iobS1jFrWjl7QYIRb5LOIzfBYxOenqzDPR812Sm3Dt+Lzzkk6Sfm37saPuaxHv/UTC4y22b0rb1iLsYS+zfUjLeKNcki4lxEVzSvEPYvJ2gdZVTZJ+ALx3+PyeKrEOzJ3QpsTFckQF0r/pmLAYSsLdB/zW9i05sab5fG0n3K8jPnMr2147VU8dYfuZPY3vKhfUf2l7rlV57bHLbT85JWq2JUR4r3emkcA0nq/t/hYRqu4jnkKv6PdOpgQKAecXE/pKB7VdtFNP7bKlj2Fl9lNbfiqVmWGzwvE6CwHaPhU4Na0ubk9MeN5JZqmspH2Ji67B6v03UyKplZ6IytutzsMhbriRQiMC27dP/ohpI6JtqlNTs8IWcW+iBaEp4Jk7gS9ttf0c2++UtAthsbsrcA6ZOiWETe3LiAqQzQhtkS6l7rfZ/lGHx4/DyWq2J0o0wG/ohuWyQ0+ky0TkcklfId4vJt4v56akITkVNS5sR+9wVjkCOGIBVfksM/W/jFEwSdh0kBk+T+W4y+wBbNRMotq+SdJuhBBvq4QK8Vkt5pJk+6Dp/J+k99j+2DTDrjEqWW77itSqlIWn2brSIt55kh5BLG6YaOvqk7MJnYzp8mZibJcB2P6VpFX7GFii9Mpv23NtEe2xBldIegjxnTuH+Nxc3iHeVExrfyVtDzwfeKSkzzfuWoFI7LV70sLxEl8hkueDyrKPE63MGxPXg231n4q2y2pM7HuVoSTNCsS5v/IfSk2oVCozQ1EnBNvP6xpD0snEl9KviX7ePUkXTJm8lgICreli+WPqwW5V0kMJl5+tAaey+oM7VDB8kEgqnExcxBwj6aS2q70NjgN+CTwXOBh4BdDFhm8Tj7faPl0Nq+2MeAMB1ecD37b9D3UUxrP9a0mLp1XtYyR1uXA9J7WEfI/x+hVZk25JjwO+TLQNrC9pQ+CFHV7fJl10RAYs1lxtVrjWdPme3zj9Hnb72ZKY5ExbSDaNZZjr0u/lieqwrnyVdhPDHNpO7kolCR+vaMESsLbG61Pl6nDNV5Fm+67UbtM21uM15pK0DfBuSZ1dkqbBrkR76XSYLBnWRWPj6aO2O9+56rXAB4GfEq/v4ZIOtv213DFO9ZQt//8e2/cOzvUKh5mFqdy97VhLaY+hOGgfS4nvIxQC2Ct0qYqdBtPd3z8S1aovJBI9A+YSlZ5tKR0PYPFGFcpLCRfFk4GTlaF/Z/sr6XeJ718oL/ZdWUioCZVKZQbokgCZoG2jGTvXqeDjhDVjqVL50gKtPxp14Zp70Zr4DnA+UTIKkbA4gbQCksHuRNLibpjXW38l7Vd7BzzW9q6SdrJ9rKRvERdzuawiaXWPt9p+WLovRzPi9NRWcxfhMLUK0EU08k5JSxFivIcSzijLTfGYyRjoSjQrwlolAoY4ihDDG1yEXZtek6zXN11cvwJYy/bB6fV4hO3c1crDgIslfZfYz90IB5Is3FL3YgrmEGNqngMGt7PFuYco5f5VlEJJwicUHtYtkp453NIpaTsyHYncj0vSVLR5zX8m6XW2x4maS9qb8RO+tjSF5pchqjcGuhu58TYZJPZT4v9iwpWuD9omGM5LLV7LSno2IUJ/evlhzWNGP9e27yQSKu8rEMuSvk98PrD9m64xS2H7GknXE5WnOU6R88UDrknfkSIqYQFutP3viR85KYtLWsL2fYSdeLN1PntOW6oa2GNi31+3/VtJy7mD2Hdl4aEmVCqVnukhAdKLU4Htnw3+VhmNl2OAyxSaCQA7A0d3iFf6ohWiB7y50nSIpJ07xPsNMbZBUmFpupVrDy46bpW0PvB/hABnLkWttm2/O1Ue3e5wwbmDEOLNZQ/CpvEtxArWoxlLdrWmcEIA4EG2Lx+qwsktXYaw9n2AeA8fTKzcnUy44LTG9jckXZHiCXiRw+koi1RxNep5Ds4Y25q542hBqVXGyWg7uSuSJHRYtU49uOlrvOxLtHheyFiya3NCTLb1Z1j9uSRNRZtkwP7AKZJewVgCZTNiVXmXiR405QCGtFckPRo4NDce0T7ZFHidSzgdzRbeRVSgXkckzH5IVIdlIelTwDGe2GK7tDbLdFtgTpvs/g6LV5dK2rx5zdUz0z5npe/xh0payoWEuYnzwjeI6yMBj5b0qszFsG8TCYu/EQs5A8H0xxJ6cLmUrgb+L0k/IqpVVlfoCb7B9j4dYlZmMVWUtlLpGU3g8jDALd0eFgQqJJ4l6UnEBbYIgdtiAq2Di1bbu3eI8SmiJPXEtOklwBNtD7c4TDfe94lJyY+JC/1nE+1TfwGwve+EDx4d77XEBHtDIkG1PPBB20fkjC/FXJrRVtttYmxn+6eSXjTqfndwvVE4Rqxu+8YOMfoSmvsRkew5yfamkl5CWNZunxnvyhTnKo+JHV5jO9ueWeHEs47tY1LF0PIOu+icWE2nnGUI29RfdNDwaVblrGn7wwWqcpD0SMacnIBulWsar1/xMycBxHTf+ravbxHrMcTnf0kiSbgi4ZT069zxTfF8895L0/jfZYCXEwkQEUmQ4zPPCb24JE3jeae9v43HbAusn27+3PZPh+5vJdI6Ir4IMd5cF5hvABsQDkcmElyXA/8D+eevSZ6vzXtmMWLf1p/yn6f//K8lhKqXIL7nvu0O+jtTJWgkrexpiJdK+iuRyPo20f48LjGRe+0m6QaiWuO3hFvcQMQ4V+S2yP42/v8rROvkaYx3s8v93pwDvHzwna5onf227SdlxnsqsBpwlsdayh9HfNddmW63FVq+yvYmSmLDkpYEzrSdtWAn6TLievK0xnf79SU/N5XZRa1QqVR6pq+EiUIc7WPAeowvUWxdOp8ukp5qe7CSWErj5WpiRXaJ9Dzz2k0KcAtjF8W5vAF4O7E6IaI64o40EbfbC0eekn4GnNtlcLYHq37nUaYlAkK/YV3iPbOhJGx/o2WMpxP9/aNcMcyYEHErJO1IVGAtBaypcGE6OGMlsKjQXIM3E8J3j5f0B+Bm4JUd4v1b4VZlgJQAyREYJT3+QGLFfV1iYrIkIRCc5cpj+7Ch+J8iLrK70KzK+TAdq3JShdRLgRsYazE00cqXE29S/Yo2yZT0/4PKkrtYMBU0014lS4mTSdtIplvxYntkcjU3XvrfxYF9bX9mkn9rbb1q+xxCPHsiWom0SjqcseO+GKE9lO1oR1Q1NisbT02/s85nJStAbD8g6ZqS3+Xpe+6rktYlEivXSroIOCq9Vm35JXCkQttlvgRNi+TCI4hFkd2JxON/p1g5emNNshLwk1Bqfwf8Mf0sRpnv0CWbCyS2/yclLLKwfemIbf8ztKmt0HLpamBs/36omrUX57nK7KAmVCqVBUTJBEjiGEIs8jOE9d5eZPYap4ukwwjby1Iit29N4/szY/opJqotcuKVvmjtw51hXtuMwrL20e5mwfwQQsRyDcavvreqdGnEO5AQjFyPKNPenqigaZtQGaz8HG37wpyxTMBBRGXAuRAuTMpw33B5oblB3JuAZ6UWqcVsz53qMVPweSIBt6qkjxArWu/vEG8XYBNCtwfbf5RU8j3+ILon9p4yqMqBeU5ES3WItzOwru17pvrHaVJEv0LSdUze6pl1HpwBWrkalYyX2g92Ir7jJvqfjxYZ1Xjafo9e0fj7PmJCe1Hukw/OWyqnv1B6wr0a8HNJlzO+giG3BWaQPHt8+vkb8d3+dklvsP2yNrFKJWgcmkdnAGekys7dCZezg93SrXAo7m8BFM5InT9fpRNSjfffg+Om/9VxiFdIOppYuIKoUOyiWTQd2n6Gj0zXbB8gFg2WJxLrufxe0paE2cFSRItllxaiyiynJlQqlQVHsQRIYlnbZ0tS+oI+SNIFzO/IMV3OkvRi4Ht2kV7A/YiJTpZjzgiKXrQCSNoKuNr2HZJeSaxofDZ35U3SuYSi/RJEdc5fJZ1ne2TryTT4IXAp0aueXbnQ4CXARsBVtveS9HDyet/3Aj5HJARKuqrcZ/s2dXQKGoUKtLFJ2o/4HM8FjlLYB7/b9lk58Wwfn8qhn0mcC3a23eWi617bljSoeOki6DucFFicsNxurZ8yRNGqHMIKfEkaLk4dKaVfsUOZ4bSm9IendF9423gXSfoCIRbenLz3aY/daowuIODZRGG9ejSF9Bd6qAApmqiW9Gmi2vGnwEc91v73CUlZrZ+lEjQpkfICIpmyBvGdl93SmmK+kBAQ/y+iGvgxxGT7iR1iFktIpQqN44CV0+2/AXt2qMx5E1HduS+p/ZuoVOyTtp/h0tXAbySukR5JfKecRRyDyn8oNaFSqSw4SidA7k6tOr+S9BbgD8CqHcb3dqJV4n5JdzHW19u27WXA7+kmEjYOh8vNUjSU4guE/TKwUbpgfSdxEXscMKnuzSSsaPv21DZwjO0DNWZvmsMyHZIxo7grVSPdJ2kF4mIu5+LhF5J+Q7gGNfevUy84cL2klxNK/usQF2ClBC1LTDRfY/tzkp5LfNb2IhIsWQkVSWsDN9v+oqRtgGdL+pPDUjOHExX97w+R9DrCXeWoKR4zGc2kwH3Anx3uCl0oXZVzJyH4ejbjrbGzqriI8+hlksbpV6Q2wGnrCDRWoZdj7HP3OGLC86PMsZFiPobQyfmJQnNoiUa11B5dYs9Ctky/m4m8Lk5dxZikCqnrefCzhDjmaTDPfWWkNfN0KVwBUqyNWZE9/yewkcNNZ5gnZ8QskqCRdCzRVvwj4ENt2/0m4cPAU4GfOHQ7tiUSNln0kJA6Enj7INmWvpuOYuyz2JYlgM8Nzp3pvbh0ZqxeSImzFzN/NXDWAoLtvxGVOJVFhJpQqVQWHKUTIPsTJfj7El/Q2xLtIVmUbn8hVo7PlfTfjJ/o5AqbbUM40fyG7krxA+5LK/o7EV/4R0t6VYd4S0hajbCr7WyxCByXJsY/YPwxbFuiPeCK1EZ0FFFy+y9C7LAVtndXCHeeSVTklOKtxHG7hxACPJN4b5fgvwvEGCRlnk8kzK5Rt3Kak4HNFA4FXyWsR7+V4rfG9qcUNqa3E4nHD9r+cYfxLQHcYvue9Pl7saRv5CZ80vnvZiJ5Waoq5zS667o0KapfQazGPi2Vk59NVNq9lMyL7XQ+eD2xerw28CjgCJIORsFJ37ynnMl4Lu/UNR2mO8beqpBK6i8UTDDMZfIEUuvFl/T9u7PHu+0172+1KFM4QbMHURX1OGDfxuvRdbHp37b/LmkxSYvZPkehBdWaPhJSwHLNyiXb53asdjwbeBZxvQGwLLEIkZugmQ5tz1unEguAc+hQ7SjpnbYP1fgW9Xl0SPRXZjk1oVKpLDj2Z3wCZDugy+R9DYft3r+IlXIk7Uqo0bcmfTE33TceDazmfPeN36WfpdJPVw4DnuMhpXggSyk+MVfSewhh0aenlZNssTRiFfVM4ELbP5O0FvCrDvHuBT5JJBkGX84msyS1UTJ+hKQzgBWcqfHicD6Z50YjadOuZfjpgvB9lElGAeMcWy6T9Ag3HFsymCPpLMJu+j2KHvMu7SoP2L5P4Zb0OduHK2mLdOA64oLV6e8uNBM+RxOJiy4JnwckHeYQJf1lx7EN+D1w6QSTida4sO4OINt3StobODxdbHd5jd9Mej8D2P6VQoshf4CFK15KxlO0JX4U+C/b20taD9jC9tFtx9WIWUSk1Q0r6zTOgbDy5ba7CLsX018oOeGe7qKL2rskFbMRLpmgsb3YdP4vY39vlbQ8kWw9XtJfiArA1pROSCVukvQBxjRPXkkkwnNZxg0dFtv/kvSgDvGKCi0nHuUC2oGEODqMb1GvLAJU2+RKZSFFIzQhRm1rEe/LJPcN209IK6pn2c5y3xiK3XUii5Kd3VTb2o6LUO//me0LFBau27i9600vSPpfQsTzbwVjFrWYbcTt8t47nckFPLOqYDS/Y8szCNegVgKjjXgDMeSbbN+qECx9ZG5SSmGt+FkigbSj7ZvVwVqxh/0d2Dq/k2hbOVwZNrVDMT8EXEshrSaFxexTgb8DF6SfC1tOcJD0Wdv7T/Re7PAevArYh9DO2tv2zyVd53xL3ctsP0VjNp9LAFfmngebFS+211a02h1hu+2EpK94PyLa6t5ne6O0v1flHr8Us7RN725E4vtc4nP3NOAA29/NjPcwQn/hWSneWYTbUVZloqQ5zrSozXy+Vt8FKm8j/EXg6yUSNNN8vrb7uxzh+rUYsYi1ImFXnqU3V3p/07Xfh4Ct06bziZanLCtxhV7PWz1mafwk4AueptvXBDFLf4aPJBLenRYhFHpP3/KYY2ZlEaFWqFQqC4hUUXEA809mW/WCS9qeWCF+pKTPN+5agcxVjkRp940mP6S7eGlxpfiU5Bn09e5g+we0d7wZSZcEQ4OfExoRRVBhi9nh8B0e+6kCzz+KIo4tA2w/QHLQkXSQ7YOIiXwuexHidR9JyZQ1CZvjXIruLyEguzvRSjiwyO5SwQVjWk33SbqbjuXztvcEkPRfhB7LFwmxx7bXN4PzSun34v7Ae4BTUjJlLSa37J2K8yS9F1hW0d61D9EqlkvpipfS8R5m+8RUSUiq6OpkP+ryIq3vAzYfVKUohJZ/AmQlVDykv5AmuPsAH8mJR8EKkGnS9rugtI3wtsAbJBVJ0EyDtvu7KvAnh235samK6+Hkf5cU2V9JyxDfR48lqhvfYfvfkz9qWuwPnCTpj+n2asR1SDalPsMa00FaAthL0k1Ey0/ue+ZXwGGK1u8TiETP1S1jVBZCakKlUllwnET0uh9FNz/6PxLlhC9kfEJhLvC2DnFLu280KdGH37dS/MGEVkkpSuzz/YTg5jmUEdzcmbIWs02yWyWcRA7VEPBMt7uK15VybBnFCwmb52xs30C8nwe3bwY+3iFk6f0tnfAprtWkcOd6GrABIbb5BaJKpe245qTfxQQ3G/HOa9y+icZrnsG7gb2JCc8biGR1jlPXgHts36ukD5EqQLpUDpWOd0dKDA6+l55KAbFzFRRpJSzUmy0+fyeqD9qO6dGEbet/EcLN3ya+l/ZMf+eyoBMMbV/vQ2yPawWTdBz5AsulEzRT0XZ/T2K8fsj9aVtuNXCp/T0W+Ddx/tweeAKRDOmEo/358cC6xHvvlyUSNYU+wy8iWquLYPtzwOcUbY8vA45JiapvA9+x/T+lnqsyu6gtP5XKAqJ02a2kJdzdcaMZ7xXEqsGTgK+T3Ddsn1Qg9j62+7bJ60TXVoYR8Q6x3cW9BE0gkOtMm85UPr+rG/3MXVDYBg9zG/DbnPempEuBZw3Gp+gzP8t2lnhdagfZgBCcm+fYAvwPtBNIVswQH2X79+l25/eLpJsZ3V6SpZFTcn9HxO6skTMi5qDKp0uMvxEiskcA59j+Tcd4WxGJskEl4WDy2eo16bGNbTngbtv3p9uLA0s7U0NG0qHArcSk/a1EJcQNtrN0jHqItylwOOG2cj1h3f0SZ7bZpZhNkdaj3dAJk3Sj7XVbxvsksCFjSY+XAtfZfmfLOOcQybdLgOcROhA/B97mDi2zaXI3H25owJQkowVm3P+n9/R1ttfLfP7jRiVohreVImN/r7a98dC2a2xvNMFDpopXZH/VaEVMidDLu1TZStoc+P3gvStpT8JJ57fAQbktbClWkc9woUriqZ5jE6JKdEPbi/f5XJWZo1aoVCoLjtMl7UOsPGU7tkg60fZuwFWSRk3GsladbB8vaQ5jYl5d3TcGF8NbE+J6nSZkIyY6QP7kcwRv6BpA0mnAd4BTuyZTYHzipNCEtrTF7JeIVq5riYnn+unvh0p6o+22dsKlxeuKObbYtqTvMyaCXCI5ulnj72WAXQn3llxKO9Q0+Srd2/aGKVHl8zBJTwSeDnxEodlxY4fJ09FEpd8culUS9tXGVtoxo3TFS9F4tq+U9AzGVrdv7LK6nRKjRV1RbB+gEJbeOo3xSNunZAxv5UaC8UxJfyZaibpWFJauAJmKaVVnpjauQfva7Y3H3ktY9+byxKHnWZwy5+uJaFuN+ldJL7R9GoDCZbCLTlqp/Z33uUqtdR2GBMBXiHMVCtvvjxNJ1o2J1/clOUELf4ZLu5hFUGlJIin6MuKa+jw6VPFWZj+1QqVSWUCk1ehhclY+V7P9pz5WnZoJEOCijgmQDxITxO+lTTsDJ9k+JDPeLxkx0XGGkJukj9p+b/r72e5mLduM+wxidfIFRGXACcAPUq9019idV1J6qHj5DvBhJ6V9hQPHAYSL1feGV+GmEa+4eF2K82Dis9apMkcLQOxQ0oW2t576P6eMsxiwvO3bp/zn6cUrUZHTR5XPCsBWhADv04CHEa4/WQ5qSqKvXcY0SezOSdEJVrfn29YiXumKlyLxUoJiQmx/b7L7p4hdpFpU4X71cNsXDW1/OvAH2/87+pETxrsG2IaxSd45zdu5K/o9VIBM6rAiaeU2Y5X0MdvvyRnLUJx5CRrGtMfmJWhyn6OH/V0bOJ5o7RLRlrmn7V+3HFfR/VVoE93RiDOIm6Vz1ay6Sd+dfx0kDLucs9LjS32GbyHp6I2ibVWnQtdqd8auAb8DfN/2HZM+sLLQUxMqlcpCjMYsYU041XQpCx4kQE4mvkB3plsC5BeEQObd6fayhBvFEzLjFZvoNC8w+yj5TBes2wGvA57X9kJkgpidJ5+lmWxyl3PBlEqEv0PoBEESr3PSt8gY3/qE2Oig6uNvxIXrRFaLU8W7gVgp/w1l3Cia77vFiIqVN3Uo/f4WoXlyP5F4XBH4tO1P5sQbir2z7e8XiDPvQljSYk56OR3iXQtcmH7Ot31LZpzBa7EbsDiRCG5WcXVudyqUFC2adFT5Nrsi8SQdk/5clai++Wm6vS1wru1JEy5TxC6SGJX0A+C9Hmo/krQZcKDtHUc/csJ4vyF0y0atmucsvvSVYCjqsJJiFnOfK5WgacQrvr8p7vLEPGzulP88eZyi+zuN55uWTbSk64GNU7XLL4HXD15TdXCzS48v9Rn+E/BlJqhUsd2qqkTRtvct4OTJkmzTPYaVhYeaUKlUFhCpBPBNRGk6hMXiV3LLl1XeIrV0AuRHwO62b023HwJ80/YOLeMUn+j0mVBJx21HolJlU6JC5a0ZcRYHjrX9ynS784RW0Q7xMWA9osUE6KTZcQLwDyIJArHPDyNKyS90huV2+pwUEa+TdDFht3pOur0N8NEOk8WiVWHp4mvAfUSi5lO2b8yMN0hmvYIo+X4XMCc34ZNiFrXZ7qPKJ01M3GUVcOi1GMZu6cY2wXOUqMgpnXQsXfFSOt4PgNfZ/lO6vRrwxY4JlSI2vZNNCtXBGrs0fU24NeawsjuQ7ZIk6eNEa8Q49zln6gylmEXPWylmp/2V9Erb35T09lH3t62GGIpdfH8nea5pXTNJeh/hSPk3YHVgU9tOlV3H2t6qwxhKfYZ711CZTc9b6Y+qoVKpLDi+TFiODsRZ90jbXpsZr7RF6m+ISfagPWVpxusxtOUe4OeSfkxU0DwbuFDJ6tnT1+04bOh2U3fCRCVIW1ZNFzVq/D0WNPPCJiUYngKcQdi3npu7Am/7fkmrSFrK9r0lqgOI1bUDgc8QK717McHKzDR5NSE6uX+KcyHw/4he7G1zAqYEyvWSjrT9+g5jA1iuecFr+1xFS0IWtn8raWtgHdvHKJywlu8QL+sYTcKSKSG1M1G18G+N0FmaLurHZntb4I1pNb5TlY9Ck+pdpNdA0r+ATzhDALuH12IUnXvoXd4x4w41WpFSxctdsyjeGoNkSuLPxESqC6VcUZaZ5L5lSzyBCgg3235PD4nRki5Ju1DQfW6iBA0dzluF9nfw3VPa6az4/k71lNP5J9sfUei1rUZUqQ2+ixYjtFQiWF61RqnP8HT1fkpXlPSi3VKZOWqFSqWygNAIFfdR21rEOxvY3va96fZSwA9tPysz3vcJ275xCRDgL9BeuFQT6HUMcEvdDklrOSxHJ902zVgHTj40H9w2Zor7PODHTvoBXZH0FaLK5TTGepu7JHzm2H6Sxqv5X2D7aSXGW5JC7RGnAFcSbT8ArwQ2s71zZrwDiYTeurYfJ+m/iLa4rJU2SSsSCa5B1dp5RJVZVjm5pH2JBMM1RA/36kRVWNbrK+lGwpmgmM12qSofSe8nWkHeMjgHSFoL+BxwmfNbFfcjEo9zCYv7TYF3u73A8iDeLsBPB69pqtTbpkuCVNHKNlxl9o3MWKUrXkrH+wKwDuGgY2Li+Oucqr9GzFKuKN8mXtujhrbvDTzH9ktzx9iIVeI8WLQCROVdkkq7zxU9b5Xe39L0cZ6e4vlKV/W2jlfwMzwt/ZvZsM+V2U2tUKlUFhz3S1rbSaguXfx3mXj/AbhM0jiL1EG1Rcak+5T0M+DcDmPLFjqdhO8yv8vISWSo2XuSvtg0IWiFpO1s/xR4ELCThtTxnS+g+Mf0sxhlVrXuVoiV/krSW4j30Kq5wdSv89JfCsR4DVEV8D1iReg8oionl12ATYgkDbb/qBC8zeVrhBXsbun2HsRkPqudwfbngc8Pbkv6HZmVQombiKq6YhfqBat89iBcHuYJPtu+SdJuREIpK6ECvMb25yQ9l/hs7EW8JlkJFUJLY9551fatKTH3/Zxg6bHbEAmVHxIrtRcCWQmV0hUvPcR7i0KgdpAUzHXQaVLKFWV/4BRFi90gYbQZsBRxrihBiZXsYhUgUnmXJMq7zxU7b5XcX4VO3UTY9ofbji9R/Dy9gMl5jxf5DE8nmTJ4iraxK4sWNaFSqSw4DgDOkXQTcXJ+DN0md0UtUl3YolfSDoTby2CynasU/3jiy3NFjXd+WIHJS67bPMd6xAre7sBtjG8rmg7PIFavRokQmjGno1YMEj+SlnMZlfj9iaTPvsRrsx2Q5YaSKGUxO4rnS1rBHVxqUolu7kX5KO617UEbTZf2ocTatl/cuP0hSVd3jAmE9oRDr+i+jMceTrxvS090xlX5EImKJYFvEk49rfAI9yzbd0nqInQ7uHB+PuHscY2GM6TtWGzEti7XXi8BNgKusr2XpIfTzeYY4rUYVLxsIim74qWPeCkhne3qM0CFbXpt/xnYUtK2hGU8wH+n5Hrzebu0C5Sw+y024U7nv50nmvxnVtedln5KUey8VXh/R32HL0fYjD+U+E7Oofh5egpKJxem3SpR+jPcgtLtHDVB8x9GTahUKgsI22crREGbK3fZFziTVVkU4KvMXw3Sls8SK+3XuVtv4brADsBDGJ+wmEu46GSRWg92Tz/3EYmfzWz/pm0s2wem310SZKPGuAWRtFgeWF3SRsAbbO+TE89jQqD/olsyb8Bttn9UIA4AGuFSI6mUS02JEtsTUxvWQyS9jqiAOWqKx0zGXZK2tn1hGuNWdNObaPLIDo+9Iv2eQ9mJDpSr8rlF0jNtn93cKGk74E8TPGY6zJF0FrAm8J40ti4JmitSy8AXiYvytzJWzZDD3bYfkHSfwjL6L0B2RVjpipce4r0I+ARRLSQyE/PEgz4GfEyFRVodOk2TiZOeTYvv01S19TpgDWCJQT7P9msyh1h6wn2ppM1dSFi6h2rW0gmaIvtre54eXDqv7Ed8D3+H+bXi2lB0fzWFTTTwzFLP1Za+PsOlmc3HsNIPNaFSqfTMoB1kqLoCYO20cpe18pYuut5JVG80e+k7u1FQJnv+e+D6jskUbJ8KnCppC9uXFBgXCveXFYkLmZfY/pWkm3OSKUNxPwoc6jFno5WAd9h+f2bIzwLPJV0spdXyp0/6iMnHtxnwPuZv0cl1gTlH0icpZzG7nu3bUwn9D0kuNUDnhAoF3tO2PyXp2cDtRKLvg7Z/3CHkG4FvKLRURDgmvbptEEm72j5J0pq2b06br8od1GCCkypw7nbSBEpl1Uvnxk2UqvLZlzgvXEi8R0xoQG1FtD/msjewMXCT7TsVYt9dko9vBT4AnEC8xmcBb84JlCplrlXosBxF7Pe/gMsne9wUlK54KR3vUGBH27/oEGMc7kGkdQranntOBS4AfkKZyr/SCYZtgTdI6uSwMkDSzYyoAHBm62gPCZpi+ytpZeDtwCuAYwnnm05ipz3s7y+BIyWNtIlu0SYzXVp/Ny8En+EFfQwrM0xNqFQq/dNLOwhwPHGRvgMxMXsV8NfMWMOUqH55J/BDSecxfrLdSttF0jttHwq8XNLuw/dnrrL9FXgU8HBgFeBXlCnp3N72extj+6ek5wO5CRVs/36o46DLBfbxROvZdXRbdR/wlPS7hPMSFHapGeK/SwRJCZQuSZRmrGuAjVKlAR3am95D6AmdTFoJ77Ca3eRs4FnEpB3CueQsQgw2lyJVPrZ/rhBnfTmRVBbhavGGUa1ALeI+IOnPwHrpYrgTqVXv3V3jpFiWtHFK2B4h6QxgBdvXdghbtOKlh3h/LplMgYlFWunPFaXtOexBtt9V7MnLT7hLOawMaH5/LAPsCqycG6x0goZC+5sWH15EtKZs4HIivKUTUl8Fvqoxm+hrJXWxxS5erVH6M1x6jKWPYWX2UxMqlUrPDNpBCPeOm5v3SVqzQ+iH2j5a0n62zwPOS8mLLFK7wdVpArB8KlP/nFu6bzT4CDERW4YQ6ctlcDF9xaT/1QLbO6WqgBcTuhWPJSZ4T3ZDwT+DxSUtPWjlkrQs3Vb0fy9pS8AKF6d9GTseOfzVdrGVSpe3mj2CsO++Bjg/tWVla6jAvNaudWy/P70eS9iemxmrWPtBirc08R5cg/Gl/W1dpv4u6RxgTUnzvb7OdPMAlmle9Nv+l6QHZcYaxChW5ZMSJ5PaxEu6xPYW042pwlbRPVQSzms/KFBRV7TipacKmisUdvTfZ3xivoumSlGb3h74gaTn2/5hiWA9JBgO8QiHFUIoujW2/z606bOp8mwyEdfJKJqgodz+voN4D78feF9joaTT9wjl93dQjVjKFruPao3Sn+HiYyx8DCuznGqbXKksIDRCw0HJxjYz3qW2nyrpTMLZ44/Ad22vnRnvWqJUe0Oi3/1rwItsPyMz3hW224q7joqzP3ARUULeWmBzms+xKjGJ2h14tO1HZ8Z5J/BC4gvZxOr7aanCJifewwgb2Gcx1i6w34gL0OnGeyaxj8O99K0mJ5JeafubSo5Sw7StQkoxFyPar05sbBOweO7rniogXg+sbHtthYbREbaz+pcl/ZqC7QepwuA2hkR93ei1n2acpYjKlOOA1w7fnxKuOeO7CHirUwuXpCcRlUPTTlDMNJKusr1Ji/8vbbl6FlFJ+P9oVBLmViBIugF4HFCq3WLed5CkNehY8dJDvGNGbHaXCiwVtumdxvO1fQ/OJcRK7wH+TffE7UMbN+dNuG1nJSyGr2XSxPE62+tlxmteFy1GJAjeZHujnHgTPMeFtrfOfGzR/Z3G83URMR7E6LK/vdhEN6o1dieu6bKrNfr6DJcaY1/HsDJ7qRUqlUrPqD+XmkNSlcU7gMNTvP07xLsvlZTvBHw+Vb90cYD5iaTn2M61Gx3wKCKp8PiU9LmY+KK7JHNlYz5s/0WhobJlqmjIjXNoGuMgAfJh22d2GJpsv6LD44fZi1gtWZKxlp+ctrOB7kUJK+cYRLQJvAU4sbHNZLjUNHgzYWt5WYr3q5Q8y6V0+8GjbD+vaxDb9xKVC1va/qtC8NAFLjb3B06S9Md0ezUi8ZhN6SqfadB21ai0BWnRSkLKt1sUq3jpI54LC30nioq09tAuUOy8muIVqQBRfw4rzQTyfUSV4m6j/3VqJkjQtD6mPe7vVLQVMS6yvylWH7bYfVRr9OFAV2SMfR3DyuymVqhUKj2TEhQ7E5ULzXL8ucB3bF+cGfdYolrh1nR7ZeBTuSt36SL/DGLS/XRCZ+Rq2xtkxhusst2bfrqusi1FXChsCWyRfm4ttUo0qoKoY7wdbP+gY4xfATcTK9wnD17rDvGuy309FwSSPkC43JxAw2IyN3Em6TLbTxmsEKdy3ivbruY3EqHPAB5BofYDSUcCh9u+LufxI+KtT1SprEx83v4KvMr29R1iLsl4Z7J/dxxj0SqfaTxfq8+1pJOJSr1Sk+2ilYSl6aHipXS8xwFfBh5ue31JGwIvtH1ITrwUc+RCgTO1RiS9lvjeHNkukBlzJWAdxreJ5badFa0A0ex3WGlWFAwSNJ+yfWNmvAW6vxkVTaX3N7tyeoJ4xas1evgMFx1j6WNYmf3UhEqlsoBQQZeaFG++L922X8RDj30EIfD4M9sXSFod2MZ2lt1laVI1zhaEi8cWhI3ydaVWMLscuwniFUnQSHoyIb62M6Hr8B3b38yMdRTwGds3dB1Xircm4WKyBuOV9rM0OxS9/sPYmb3+kg4FbgX2JMa5D3CD7fe1jDOq7aA5vlZJTEnXEZUTSxCTppuIyXvXyefFwPsGJcqStgE+ajtLRFahl/J24DG2X6dk+94lUSjpIttb5T4+4/naTk5KX6jvQDi2PJqxSsIPuaCWURcmqshzpnZWD/HOI4S0vzJ4HSVdb3v9nHh9UrBd4LWEpe6jgKuBpxIVmVm6O6Un3ClmMYeV9N1+ILGQA3AeoTnXKSlVkpL7O43nKrq4k/H8XwS+7gK22Kla4/3AYaOqNSStONOvcx9jLHkMKwsHNaFSqSwgJC1DWHIOixPmVpRcQyQ8/plurwycl1OBkEodz7T9rJyxTBBThDXgmrY/LOnRwGpuKfqaVvGfSFT0XAZcClzqjj3GI56nqyDtcLzSCZqHAZ8GXmF78cwYvwDWJqpeSkzgrwGOZsg1yJmaHaVR6LLsDTyH2Nczga8684tP0la2L5pq2zTiTNpW1mHyec3wqvOobS3inUDou+yZqgOWJSZ2G2fE6qXKJ8V+BFFGbSIh/H+N+9ZvW6GTquEel27emFOVk873bwQeS3w+jnZPGlD/yUj6me3Nm+dTSVfnvAcbMUuLtA6+Q3cgEiqPJloXtwbucMuWhpRw3Zz4nttY0Tb8Idud2u1KoQkcVjok0k8GridshCHEXjey/aKJHzVpvKIJmtL7O43na1tVV3p/e9NpKkXpz3APVTlFj2Fl9lM1VCqVBcdxhJL4c4GDiWRDl7L3w4CLJX2X+GLZjXDWaY3t+yXdWXi14EvEJHs74MOE28MXiQvFNqxOOOX8CvgDcAtRdZCNxmvZNLc/Cjo7SAx4Q9cACtvRXYiLubWBU+jWf9tZr2OIu21/vlSwRkXE6rZf37UiwvYDhNtIa1veCTic+XvbR22balzzJUwkvd521778mxRtU8el268kkme5rG37pUp25bbvSonSHJq28XcSSa4B2fbxaTX/g0SptoDDJR1s+2sAGcmUbYiJ3W9SvEdLelXGavSxhKDoBYTuyXpE1UGlHX+TtDZp8iTpJcCfOsYsbdPbbBf4aCMx/wmFyHFb7rZ9tyQUrnG/TNUvueMrXQFS2mFlbdsvbtz+kKSrO8T7GpGgGeiw7EG0YmUlaFjwrlBtz7Gl97c3naaCMUs7G5UeY+ljWJnl1AqVSmUBoTEdh2ttb6jQJjgzt4w3xVyPSFgIOLtLK4ekE4nS4h8zXr8iVzvgStubDq0sZq2Wp0ncEwn9lC2B9YF/EKvlB0722AniDVo4Vk3xfppubwuc23ZlbKIEzYDcBE1ahfk+cKILtoul2J0n8JJeTrSsnMX4aoMrM+MVq4hI8bYCDmKsVHuwStRqFUvSFsT7ZH/gM427VgB2ya0AGXqOzmXeCt2FDxEr4xBWvx/KreZKLUTPBC5Kn+W1CX2I7KReqSqfxmNvBLZ0Et5UOJpc7HwnijnAywftEAoNj2+3Xb1UQ69Iod1z+UyW8S+sSFqLEADdkhB6vJmo0suq4prkebJcUXpqFziFqHTZn/h+/yewpO3ntx1file6AqSow4qkS4ADbF+Ybm9FtCRluYmNqmDqUtXUw/5OKmIsaWW30A3rYX+P8wib6OFtLeItkGqN3M9wemzpqpyix7Ay+6kVKpXKgmNQNn6rQjzy/wjtiWxSAqWIHgbw3+mnFP9OZdCDlcVVaLSFtCG1aFwv6VbCZvY2orz6ycTKW9t4e6Ux/QBYz/af0u3ViCqatgxW30cmaMhcfQfWsm1Jy039r615I92dCjYgLs63Y7xrUG6SsGRFBEQ70tsYsiXOYClgeeI7s+mecDvwkg5xm3TZz0HLwUku0LYn6SzbzyGSUWcQVRrHE/pFr+4YvkiVT4NbiHbAAXOB32fGgpi4zqsqsP0/KfndlnltQrbv6/Y2XqTZGfghcA4hqHoH8KxUon91TkAVdEVJ5+edbX94gvtbV4HY3iX9eZBC/2RF4nOYS+kKkNIOK28Cjk2VNBAJpFd3GN9dkrYeStDc1SFe6f39JXBkSrTOJ2LcJpmSKL2/T2zeSN8tXdphildrlPwMJ0qPsfQxrMxyakKlUllwHJlWkN9PuP0sT0vbwj5xpujiJHyeaFFZVdJHiInn+9sGkbQvkaTYipikXARcQpS5dnVHWWOQTEn8mTHthGnTQ4JmwFMlHU28V1aXtBHwBtv7dIg5oMQMbxci6XNvgVgA96aqlEESbm262dfeZvtHXQflMbvbY4jKKNu+Y4qHTYmkTWxflW7uOOk/Tz3Gkm17q6SYZ6WKjacS75f9bP8tJ2CjymcVSW9v3LUCkKUJlPgDcJmkU4n3zU7A5YPnsP3plvGuSJ+5QdvUK4iEXFs20nir1YH1at820f9pbJZ+TiOO3SuAnwFvlHSS7UMzYha16aVwu4CkpwI/tz3X9nkKG/RNSPbvGZSecJ/GeMfCTqTE2EaKFlds3z75I6akdIKm9P5+FfiqxkSMr5WULWJMof1VfzbRh4yq1iAWY3Ip/RkuMsYej2FlllNbfiqVCtCbUN/jiZaBQUtSa80YRX/6xUTbwYS985JWatvaIOkLRMvKt4l9fxnwa9tvbTvOFG+c+4RCFPVaZzpSSLqMSESd5sIOF5IeZfuWjjFOAN5q+y9dx5PiPZtIuq1HtBFtBbza9rmZ8T5OTNa/R8eWJElvAt5DWIFDaAJ9wvaXcsaWYp4DrAacRLg3jSwBbxGvSNuepJuA/zfR/TktbJKeAWxDVEYd0bhrLnC67V+1jZniTlqhZvtDLeMtDbyZaJsS0Tb1JS84/YRKA4Xd9IsH7RaSlge+SyRz59hebybHB720C1wFbJoqMwffI1fktoxJ2pho9xk34bZ9TU680kj6KHCo7VvT7ZWAd9huvQAzFLdUgqY4Kihi3IhZZH9V2CZaQ+2sad+vmw2f3QGlx1j6GFZmPzWhUqksIPq6aCiFQntgwDyRL9udq2hURnBzqufI0qCQtAtjYn3n2z6lwxhKJ2gus/0UFdChSY8t+h6UdC6wIbFi3ExYZLsfpPfhoCLi0tyKiBRr1Gqf3VK3SNL7ieqKt9i+KW1bC/gccJntQzqM8RHEytpLiWqNE3LjqZDlr6S/A6cyuorJznQmS7FXp2CVz8LEgjgP/qehcCbbaFAFlxJeV9t+gjKd1FTeFaW0VfQoTYxrcxM0jRilJtylHVbmex1zv8/TY0t/z5Xe36aI8dFuuAtKutEt9Z/6uLZUAZvoZrUG0TYFjWqNLgmHUp/hnse4wKy2KzNPTahUKguI0hcNCwJ1EPkaitP7fna4uH4MsI7tnyhcZha3PXeqx00Sr2SC5ruEVfIXiCTDvsBmHVawSl+4PmPUdre0Tdb4fuhR8bJEbkuhED7dyPbdQ9uXBa6x3bpNbMRzbAC8E3ip7aUyHr8zyaLX9pkdx9LL57VklY+kz9reX9LpjJ7stErqSTrR9m4Ky9pR8ToLKM728/1sROFatQuR4IOYiJ5GlPwfafsVGTFLi7SWFvH8HqG99eW0aR9gW9s7Z8YrnWAouvgi6Vpg80EVWDqvXmH7iZM/csJ4pb/niu2v1IuIcen9LW2LXbxao4fPcOmqnAVqtV2ZeWpCpVJZQJS+aCiNRot8valtNYTC5vGeoW1ZyY6Wz9v6AkLS64DXExdHaytseo+w/cwO4yiWoJH0MKIK4lnEqslZhI7F3zPjFXsPqmM701CsyfrGcypKXmn7mxqv1dEM2EpXY7JVQ0m/tP34NvEaj30CUZnyEuDvwHeAk92yhUrSlwgRvIErz+meQCRzmvGKf15LV/lIepLtOQWTeqvZ/lPpaoOh5+j9PPifiKQnMdaCdaHtKzrGK+2KUrpdYFVCg2w7Irl3NrB/2/NCI17vizldFl8kvRN4ISHQauA1RJtrjj7OArnW6ri/c9zSNWyKeKUTUjcCGw5fx3UcY9FqjdKf4dJj7OMYVmY3VZS2UllwfBM4WyFsObhoKC0E24VSIl+XAJsOrdB1EtzskTcTTkGXAdj+VbqYzaKZoAHWBh5J6EVkJWgc7S6tV2Anodh70PYDkq6RtLrt33UZlO1tuzx+BIMKiC6q/01ukfRM22c3N0raDphQ12caHEO0hz3H9h87xHk6sTp3f0riXQBkJ1SYphCfpEs8fWvTwQrivCof2zdJ2g24BmiVULE9J/1ulTiZJN7gddzH9rua90n6BPCu+R/Vmtl6HpzVpNc6Rxh4IoqItKonAcqUOMmqQpyAxZsLHWnCvXRusAkWX7LPtbYPTUmBwcLBhztW2RW91iq9vxQWMab8teVNwJJ0E4Sfx0TVGoQ+VS5FhZZ7GGPRY1iZ/dQKlUplASJpe8ZEWs/qWpo/G5F0PfBJwsHogOH7nSFoOc3nbb36qyGNEoWN4ZW55f0KK8onEyvuA82T62xv0DLOZKXE7lh98DwaFS9d3oOSfgpsDlzOeBHUtu0Wk5bp9vWemS6Snki0HFxITOxM7PdWwE7uKCbblREr5AuktaTNZ67HKp+tCHvnwcriQBA0V99gvmOnDvoVCs2PFwNrMH7l8+CceJXuqLBIa6l2AUnvTMmFwxnddpZl09tDBUizonCw+PIpN+zGc5G0g+0fFIhT8nuu6P6qsIhxillyf08GNiIqozrbRPdU8bIxZT/DRcdY+hhWZj+1QqVSWYA4LFw727j2gcoJ9b2RqKp4CPOvyJpwXMkZ39rALbbvkbQNIYb6Dae+cPKqQM6TNFhhfDbRq356zvgS99i+V9JgzEsw4sJ4GowS61wO2Bt4KN2qD34B3DdoSZL04NyWJKCVg8okDN4nqxJtIT9Nt7cltARy3zOrAK9j/slsK1FV2z+XtD7wcqK1ZuD+8gYP6apMc1wT6XXkXlg/Pq3wDmKsnW53vlCfgjbv7b6qfI4G3kYkuu6f4n8nRKHvsg+wVuNYQqxEX9RhfKcCt6Xx1dXKWYAL2/Tafk+hdoGBC16nlqZhSleA9FBR2ORgoHNCxfYZwBkpQdNp4aqH/d2+cLyi+0thm2h6qNYo/Rmm/BhLH8PKLKdWqFQqCwhJcxmbgCxFnLzvsL3CzI1qDJUX+drb9tEFx3c1UWq7BnAm8WW1ru3nd4i5GJGkeA5xoXkm8FVnnhglHQrcCuwJvJWYoN1g+30dxvhgYL80zhMJMbvcXvrimjElkfQD4HWD9gtJqwFf7PAevJhofxk32bZ9coHhjnq+abXAqLBex0RxcuO1eN5pV8L0VeUzqDLLeexQnBWBlYCPAe9u3DXX9j86xC1ic14ph8qLtC40ApQlKkAKLr6Mit260nSKeJ2r9UrvrwqLGA/FmXXC131Ua/TwGa4VJZVO1IRKpTJDKFw5nmz7vTM9Fign8tVX+8bgQkHSAcDdtg8vffHVlZIJGkkrA28nqn2OBT5n+58dx3c1BVqSGvGeChwOPIFIEi5OhyTh8ORTHYVvc96/XWjZArM4cKbtZ/U8rOZzttE8mU68Vp8/Scswvsrn58DxmVU+g0nDbsT77nuMvxDu5Ayl0FJaphEvSydI0pHA4bav6zKeSjlGvW+7TER7aBd4HPD/mL+yrpU49wSxSyQYii6+DMV+shs2wgXidb5G6GGxqaiI8VDsEvtb2ib6VaO22+6ia1P6M1x0jKWPYWX2U1t+KpUZwvb3Jb176v9cYJQS+ZpMeDG75Qf4t6TdgVc1nmPJnECTtFzEIDNbJGw/AByVfrKR9EngRYSw4Qa2/9UlXoNSLUkDvkCszJ5EVA/tCazTId65ks4khFqdYk/mADQVP5D0fNs/7BCjDdM+lg4B2TuVYZPZgWWm/pdWtFpRTYmTr032Py2SPocN3d6s+VSEQ0prJO1IWJX/F/AXoo3jF0QSKIetgVenC+x76L8NqzI1RUVaKd8ucBIhZv5VOrSxTYAKxFjb9osbtz+UkvVZKIS03wGsbvt1qXJy3a6VNIk3FIhRZH/Vk4jxECX2t3kunWcTnRusS+JkEop+hnsYY9FjWJn91IRKpbKAGKrcGCjFz6YSsTcBx6byVgiRr5FZ+8mwvVfRUY2xF6HP8hHbN0tak1C3z2G/9HuHEgPrIUHzDuLi/P3A+wYJEMYmY7ltYueprGYMtn8taXHb9wPHpDab3FhvSZ+Tp6VNR9o+pW2cRnudgPdKugf4N92PX2nuBq6T9GPGi/r2VWbc6nyTXotPENo2Yuj42b6++AinmfRxfzoOhwBPBX7iEKreFti9Q7ziegmVzpR2RbkTuFpSqXaB+2x/ucN4JqPEhLuowwohljsHGCRSbyGSSlkJlR4SNEX21/bHgI+pkIjxgNL7a/vvQ5s+K+lCwmggZ3x9VGuUdnIqOsbSx7Ay+6kJlUplwdGs3Bgoxe80M0MZyS+AQwm734cQQoo7A9dO/JCJkfRw4KPAf9neXtJ6wBbO1FWxfQOwb4q9EvBg2x/PjPWnVGZ7dKGWi6IJGtuLTef/JK3Usg3o3URL0nXEhfUPbXepprlT0lLEZOJQQlx0uSkeMympJayTq4/tUnbJbWm7+vvf6We2ciiwo+1fTPmf5Wib9NmPmJDNJSrDNgXebfuszOf/t+2/S1pM0mK2z1HYJmfhpF8z3EJUmTlc3qa3tADl6ZL2AU5hfIImS8unhwTDqMWXV2fGgqgAeWmqQMX2XWqsImRQNEFD4f11ORHjAaUTUqVtootXa/TwGS46xh6OYWWWUzVUKpUKAJLOIARVr2S8gOdwaf104/2I+KJ/n+2NUnvJVR30Os4lrB+XAK4G/gqcZ/vtOfFSzNOAPUq0XMyQJkarnmFJz7L9k6Ftr2pb7ippCdv3KcRQ/0zop7yNsDD8ku1ft4nXiDtpRURGvF2Anw5eX0kPAbax/f2ceNN4vvWnW7Wh0FB6LNE7v0Ds09v210u6yPZWfY5pxHO2fU9fk84vzwXeDHwAOKZDL/1PiETyx4CHEW0/m9veMjPeC4n2pHEtRLZzW4gqBVEhm96SpNXyYdxBw+IEYsK9p+31U3vEJe6oL6VCDiupqvGZwEUOnbS1gW/bfnJmvCtsb9Y83w3OEx3HWWp/i4oYl95f9WiL3XiOC21vXShWL5/hLmNcEMewMruoFSqVSs9IOpxJVl17LO9vy6NsP69gvIfZPjH1DZMm4F36wVe0fbuk1xITpgM13t40h2ItF54ZTYy2q3gflPRiYrXywUSP/j20L5W9HNjU9m8lHW77rZSxUC5dEXFgs2XI9q2SDgS+nxOsVAuMpC8RmhwXAx9WCDF2scKeLm1dJK5Ik7HvM36lvFMF0RS0fU8P/v/5xHnhmo6r2zsR54W3EYLQKxJWrrl8mLItRJWydLbp7aFdYM0u4xlB0QoQFXZYAQ4CzgAeLel4wvnr1bnjA+5NSSOn8a1NB32bHvZ3F6JCqJTmTtH9deF2ygVQrVHiM1x0jKWPYWX2UxMqlUr/XJF+bwWsB5yQbu9KrBrNFi6WtIHLuVHcIemhjH3JP5VoI8plCYWN7m5Atg3xEKVbLma1JgbwDCKZck26/UHb38543ubFeMkKhj8Xbi8Z1TrV5XuvVMLn6YRLxP2pHP8CYuLdiVIJnwYrEPoQz2ls6yIsPRjnIwi3KQM/s/1/jbvbJn3mSDoLWBN4j8Jm/IHcsdm+o3GzhFBh0RaiSnFKiLQWaReQtJ3tn2oCp7wOicyiE25gezfcCW3/U9LzCc2v1tg+S9IcIvEoYD/bf+swvoMom6Apur+UFzE+iIL7q/K22M0q50G1xm654xtBic9w0TH2cAwrs5yaUKlUembQTiHp1cC2tv+dbh8B5Pb5F0NjQqpLAHtJuokybhRvJ/rK15Z0EbAK8JIOQz2YsCG+0PbPJK0F/Co3WGrR2aNwi85s18RYCXgK8L/Ao4DHSJLb93721StauiLiCkmfBr5IjPmtdEtilkr43OsQ8cX2nR0rKpoUrfBxDwLTqcLsg8BPiXPM4ZIOtv219Jxtkz57AxsDN6Vj+VBCwLrtuAZCxvM2MSZsnN12BtwqaXngfOB4SX8hLtgrs4POIq0uJ0D5DOJzMcopr0si8yDKJhiKOqyk1ttvA6cNJTSz6CFBU9oVqqiIcQ/7+zXCJnqQUNiDaN/OsoleANUaJT7DpcdY9BhWZj9VQ6VSWUBIupEQZf1Hur0ScKntdWd4XI+Z7H4nUcXM2EsA6xJf8jcOkkmzBS38Girzeqan+f//A3zc9tfSReEngM3cUh9C0p3Ar4nXde30N3RMwikU+4ex7ddkxluO0NQYCNedBRySe9Eu6XPAI+iY8GkcPxh/DLsev6KaJ5IeBRxOTMAMXEhcrN/SIeaNwJaDSWhKgFzc5TyosgKPRUnvwbuJ13bQQnT8iEl4ZQGhwiKtE7QLvClXw6IP0udsMOG+tMuEW9I7CT2zpsPKabYPzYz3DOClwAuIdtITgB84bNZz4hVN0PSwvyPdE51p3dvD/l7tIX2dUdtaxCterdHDZ7joGEsfw8rspyZUKpUFhKS9iBP2uWnTM4CDcr9EZyuSNgd+Pyjjl7Qn8GLgt8T+5joVLEOsRj+RhltG7mQ7xTyRuMgs0qJTMkGT4q0N3GL7HknbABsC32j0cq/c5nhKWt3274a2Pb3t5LPPJNxsplTCp6/jVyrh04j3Y+BbwHFp0yuBV9h+dk68FPNsooT+3nR7KcJtKisRmdpnXkohgccUc2tgHdvHSHoY4Sg2Sii0shCiwiKtKixAqRDP3hNYg/FJwi7fS8Um3Cnm82gkql1AWDstSmwHvA54Xm5VWOkETYpZfH9L0UNC6hLgAI+3if6U7S0mf+SE8U4mqjUG17p7EC2v2dUaPXyGi46x9DGszH5qQqVSWUCksv49gP2JEtyrgUfYvnzmRlUeSVcCz7L9D0lPB75DtFpsDDzBdlbbj6STgF8CLyfaf15BuGXsN+kDJ49ZeqWodILmamK1cw2i3ek0YhXm+S3jvNL2N9PfW9m+qHHfW2x/IWd803jeS9pcQJROmklaBXjniHjb5cRb0GQcv9IVPsVX2SR9A9gAOJVY7d2JmAT8Txrsp1vGuxHY0IUEHhWixZsRn7PHSfov4KS2lT9KDhETtRJ1aCGqdEQ9ucCUQuF6cylhbz9PD6jD91LxBEMjdhGHlTQh3pEY56ZpfG/tGLNIgmYoZuf9VWER40bcUgmpjYnEwjibaNvXTPigyeP18T1S2tmodFXOxhQ8hpXZT9VQqVQWHF8iLo6WtX1aavk5Gdh8ZodVnMUbVRMvBY60fTJwckoQ5PJY27tK2sn2sZK+RSQZsumhOqi0hsoDDnekXYDP2j5c0lUZcd4OfDP9fThxwTrgNUAvCRUaSYxpchyRNHsujaRZh+c/npg87AC8EXgVYbedRR8tMFPQ6vi5vObJ3yS9kljdhnCn6dqq8r/pZ8Cp6Xeuo0JpgcddgE0I+3hs/1EhdNsKJ7tN2yXdLCplKO0CU7qlYRnbb88dzzC2zwPOG5pwf40Qne5KCYeVEwhtrzMIvatzbWcLS6eYwwmaUt/1nfeXQiLGTUrur+2rgY1UyCYauEvS1kPVGnd1jFlaaLnoGHs4hpVZTk2oVCoLjqfY3nQwIXYoxS8104PqgcUlLWH7PuCZwOsb93U55wz0V26VtD7wf0TlRjap7/ZjhPtSs4Ih1+6ydILm3wqry1cxJlS4ZEYcTfD3qNslaVsCWTpp9lDbR0varzGpOK9DvGOIFphd0+1Xpm3ZLTBT0Or49ZDwGSTbPpPiXZy2ZWO7hL12k6ICj4RgsCUNLtSX6zK4qdr2KjPCQZQVaS0tQHmcpNcRE/fmezqrXRZ6TTCU+P44Bni5k1B3V/pI0DTDdw3gciLGMaDC+6vyNtFvAo5NiUdI1Rq540scRNnPcNEx9nAMK7OcmlCpVBYc/04rRIML9VXoYO85i/k2MXH9G5HhvwBA0mPpZpt8ZPpS+gDR+rI8mRcgDY4hVhY/A2xLuINkXzCVTtCk8bwR+IjtmyWtyVilSRs8wd+jbs8kpZNmg3h/kvQC4I+Eu1Euq9huttV8XdL+HeKVpmjCx6G3k61F0kTSZ23vL+l0Rpe75z7PaemnM6kt8weSvgI8JE1qXwMc1SHsycBm6fx3NDHWbwGt2vYq5XB5V5S1bb+4cftDHasx7wU+CbyPsc+KgazvkZ4TDNkOK0o20cCDgJ00ZHbmfHe3ogmaITo7ymi0iHGXSrbS+1vaFvtqCldrlP4M9zDG0lbblVlOTahUKguOzwOnAKtK+ghhIfwfd3K1/ZG0WrwaId42uCBcjNBSASJjb/ufLeJ+Nf15HpkXliNY1vbZkuQQAj1I0gVEkiWHogka2zcA+zZu3wx8PCPU4yVdm8aydvqbdLvUsRxF230vnTQ7JK04vYOo3FgBeFuHeH20wExG2+NXJOEj6Z22D5V0OKOTHznVHwNh209lPHZCUiXTUsDj0qZsN7FUmbIz8C7gdsKh7IO2f9xhiKXa9iqFUHmR1tItDW8nqvW6JHmalK4AKeWwUtQmuq8ETcH9HXBY4++BiPFuo/910nH1lZAqbYtdvFqj9Ge4hzGWttquzHJqQqVSWUDYPj5l1J9JTJR2tt1FH2LWYvvSEdv+Z2jT2YzX8hiJpEl7yd1SxHKIuyUtBvxK0luAPwCrdohXNEGjcuJ1T8h5/gLs0eafSyfNGhe8txEJrq4Ub4GZglbHj3IJn8F56YqMx47E9pz0u0vL1XykNppjiUmJiBLwVznfNvkS4FbbBxQZYLm2vUo5DiNaXz4uqYRIa+mWhp8TrWyd6LkCZA4wEMy+BTiJltoitgffiwd7yEUrVWO2pWiCpkGR/Z03ELvEdxH0t7/fBM5WiJwPbKK7tIj1Ua1R+jNceoylj2FlllNdfiqVyoyghjr7FP83aTKiiyaDwuL5F8BDgA8TiuyHjkoITTPeRcDTgO8SFzp/AD5ue93MeA9t3JwnXme7a6vTRM/X1lXmRcAniCSUyHQwKZ00k/T5KeLl6msUpdTxa8RbnUj4bMFYwmc/59sw72r7pKm2tYy5FdH//hhiUWewz7ntDHOI1fcb0+3HAd+2/aTMeDcQ1S6/ZbxT14aZ8dYj2vYusf3tNFF8qe2cSrNKQVTYBaZUu4CkUwhnsnPooAsk6UO2D1R596/SDitX2t50aNucDp/hNUclaIa3tYhXen+LihiX3t/0+GI20akidvOhao0rbD8xN2Yjdilno+JjLHkMK7OfWqFSqVRmimllc7skTKYR+2fpz38R7Tld2Z9YDdyXSNBsR6xMZ+HC4nXToK0rz6HAjgUqrZr9428AvtIx3hsJkcgTCd2UTkKCPbXAQLnjNxhHMc2TxHuIldiptrXhaKLtag5QogVhyUEyBaISTlKXCpDtC4xpHsNte0SJfx/aDpUWqKBIaw/tAt9PP53ooQJkQBGHFUmPJxJHK6bk8oAVaP9d1ORk5q9+/S6QlaChvKNMaRHj0vuL7TOAMxQ20V0TAb1Ua5T8DPcxxsLHsDLLqQmVSqWyUCDpWGK1/dZ0eyXgsJxVttR/OyHOFMgsnaBRefG6qWhbsvjnEsmAZtJM0s4FkmirEdU8LyUmsCcAJ7uFZs8QxVtgEkWOX+mEj6TtCdHURw5V+6xAHM8u3Gb7Rx1jNLlC0tGMabS8gkjWZJFbzTMZkh5GvB93Bx5JaGlVZgiVF2ktLeJZTBcoUXrCfRBlHFbWJSztH8L4tpW5RMVBK3pM0BxEWUeZIiLGPe5vk8420em76VrGqjU+3DXBUPoz3McYG5Sw2q7McmpCpVKpzBRtqwY2dMNqNF20TtkyNAFbAL8ntCYuyxjLOPpK0DC/eN3NZIjX9cgV6cLm+4wvTc/t3YYCrkOpsucI4AhJjyQmsj+X9C7bx03+6JHxTk9/3jmqBabDUEsdv9IJnz+mWC9kfHJiLpmivo3k4DmSPkn09zf3+cq8ofIm4M1EFYiA84EvZcYqhqQHA7sALycmxqcAa9nu4jJVKUNpV5TSIp7bUEAXqK8Jtws5rNg+FThV0ha2L8kdT4OiCZoBpfa3QSkR4172d4gStth9VGsUd3LqsaKkyDGszG6qhkqlUumFVBZ7i+170gXihsA3GhUmK9v+R4t41wDbDKoMJK0MnGd7g4yxLU5Yye6exvXfhO7Cz9vGSvH+yiQJGmcKcUpay/ZNQ9s69UZP8XzzesSn+f9Fe/NTzPn66TvE2pR4jZ9NJAYOSy0YxcbWZbw9aBsU1TyRtGTHlfFmrHMmudu2tyvxPLMFSXcBlxNVChfatqSbnG+hXumIkkjrUHJhHrmJYEnvJJKPzXaB02wfmhmviC6QpJ2AndPYmkn/ucB3bF+cOb7SDivLAHsTyZ95iZ4O58FSCZpBvNL7uzGRMBsnYmz7msx4Rfd3KPaTbV9eMF6n7/e+PsNDz1HsGiTFK3oMK7OTmlCpVCq9kEpYNwPWAM4kLujWtf38zHh7EvoN3yUuWncDPmr7Gx3HuTQx6f4k0Wt+eEaMogmaRtyiYn3TeL71bV/fR+wpnvc6xipTHgv8enAXMdluJQgq6UPEyt0vgO8AZ9jOblVptMDsRrQPDVgBWM/2k3Njl6SHhM86wMeA9Rg/0ZnxpICkE23vNvTemUfb90xpJL0NeBmwHPAt4n3z49lw7BZV1JNIa4pdVMRz+P07aluLeKUTDM8g2ilfQCQNOzmsSDoJ+CVRzXUw0bb3C9v7ZcYrnaApur+NuKVEjEvvb2mb6GbsVos2Ix7f22e48Rydxphi9HYMK7OTmlCpVCq9MJjISToAuNv24QW+TNcjhF4FnN2x2mBp4gJpdyLpcxrwNdt/yI3ZiNs1QTMo1T4UaNq3rgAc4EzleZV3lXkUcDjRU27gQqIc+paWcR4z2f1uqWsh6QHgJsbKqAdfdLkJmo2AjYmL/aYg8FzgHGdqsxQ8fr0kfBQCyAcSNtE7ErpA8pjYZU7M/YiV/LnAUYS2w7ttn9Uyzmq2/zTRe6fte6YvJK1FnA9eBqxDHM9TPL+NfGUBMarKr1TlX2oX6DRpkvQ14nzQ1AVawnaWLlfpCXcjbimHlatsbzJIGilEpc/MrVornaBpxC21v0VFjHtISJ1AVHTuaXv91MJ2ie2Nc+INxS5SrdHzZ7jzGPs8hpVZiu36U3/qT/0p/kO0vuxOqNmvmbZd3yHe3iO2fTwz1rHEl90hwPqF9ndpQqX/JOBnwAeAR2bG2omYdP49/R78fB7YssMYfw08oeBr/GNikr1E+nk1sQrf13vqkmn+32Mm++nw/EsW3p8ixw/YiHCT+m36Pfh5EbBSh/HNSb+va2y7oOM+X5N+P5dIYm4EXNkh3iems202/AAbAB8F/nemx7Io/4x6vw3e633EzoixNPB2QmfoFEK3aOkO8U4iXOf+N50XzgI+13GMyxIJ3JMJba/DO8S6PP0+H1gfeBhwU4d4V6Xf16bfSwI/nUX7e1XJ903p/SXsgseNc3Dezoz3IOJ66Kh0ex1gh46vR9HPcOkxlj6G9Wf2/1RR2kql0hd7Efa1H7F9s8Km8Zsd4r1E0t22jweQ9CXyhf/2AO4gxCL3leZJnmRVbCgciNYHfgR8yB3bZlxerG9AEVeZBqvYbpbefl3S/gXjDzMtIUVPszpB0iW2t2jx/GtIKtkCU+T4OXrvr5H0LRfSPEncLWkx4FeS3gL8gahu6sLgw/Z84Bjb16jxAczg2cC7hrZtP2LbjGP7OuA6YJ4jTMZ7sJKJFowrSmcBSofu2HHAcbb/WmBMj7W9q6SdHA5C3yLacLNQeZekI1OVxvuJJOvyxOQ2l8E58FZJ6wP/R1ShZtHD/hYVMabw/lLeJvoYYgFrcJ67hUjyta7k6vEzXGyMidLHsDLLqQmVSqXSC452nH1hXknrg21/vEPIFwGnpXaO7YF/2N4nc2yLTef/JK3k6bVzFE3QNLhK0pspV6pd2pXnb5JeSQj2QVQk/T0z1nQo3aPa9gLsGMZaYLYltcB0eP7Sx690wmd/YuVuX2KFeztihbsLcySdBawJvEfhhtN6ciLpTcA+wFoKu8sBDwYu6jjGBUmpiXxlahaEK8obch+YEosHAm8htWRKup+ohji4w5hKT7hLO6wcB7yYGNOxadvDO8QrnaApvb/fBM5OOiADEeNjJ3/IpJTe34MobxP9Ukm7A9i+q0MSva/PcMkxQvljWJnlVA2VSqXSC5LOJdwFlgCuBv5KuPK8vWWclRs3H0wkAy4iaVm4hVNQW1Re7X26CZrB/5fujS4q5CZpdeALxKqOgYsJDZBe9Ct6eD1axVMSBJZ0nZO7lKQLbD8t8/mLHr8+NE9S3BWI98ncLnFSrMUIPZqbbN8q6aFEa9y1kz9yvjgrAisRornvbtw1t89zQmlKv6crU1O68k+FBCgVQsbPB17vpAWRNHi+TAhrfyZzfK8lWlU2AL5OmnDb/krLOH25JJ0B3EZUCMxLWtg+LDPe0owlaJYcC9cuKdXX/qbYJUWMi+zvUMyHMmYTfak72ERLuhh4JnCRQ1dvbUK0P1vMvYfPcB9jLHYMK7OfWqFSqVT6YkXbt6eLuWMcyuytJk2JOYyvTBAhJvuCtL1P14zO5dtDnE2IcE6XoqXazhQ1nCTe74ik2YKi9OvRlqItMD0cv2Vtny1JKSlzkKQLiCRLayRtRqzOPjjdvg14je05uQO0/YCkPwPrScq+BrF9GzEJ2z2NbVWi2mN5ScunY1upjKJ05V+pdoE9gWc3J162b0pVbGcRidIcSlWAPAP4KeMrAwaY0HzJ4VG2n5f52FGcyliCpkubRV/7i+0zgDMUIsbZ3+mJUvsLgArbRNNPtUbpz3DRMfZwDCuznJpQqVQqfbGEpNUIIbf35QaxvWaaxG5he0GX8pcu4WubECjdC17KVeadtg+VdDijLWv3zR3jFOxROF7b12N/CrTA9Hj8SmuefA3Yx/YFAJK2JiaP2ZbEkj5BWJDewNhqtAlBypx4OwKfBv4L+AshPPwL4kJ7YWCmk4SLIscRlX/PpVH51yFeqXaBJUetYtv+q8L5JpciE+5GpdvBHuGwkj88Lpa0QdIYKkGRBE2P+9vkYPJ1OgaUTkgdRpyjPy6ps0207bMkzWGsWmO/AtUaRT/DPYyx6DGszH5qQqVSqfTFwUQ1xYW2f5ZKl3+VEyitan+KsRXAhZW2CZo+esG/Beyabr8ybXt2yziDC5crOoxlPjSFrbM7iv2OoFWCxvbP0jgXA/bt0ALTy/GjvObJ3EEyBcD2hZK6tv3sTLRDlBLoO4S4CP6Jw3p1W1LVymxBYe28ju2fKIQKl2i8d0onCStTU7Tyj3IClPdm3jcVpSfcJzN/peV3gSdlxtsaeLWkm4njlmVv36B0gqb0/jYpkVAtur+2zwPO03ib6K8Rwq+t6alao7TQctExlj6GldlPTahUKpVesH0SUfY8uH0TUXacy1mSXgx8z15g4k8zvXp8dtJcOZ/U2tRxZayUq8zp6c870+s8D0m7jnjIdDkU2NGFnIhKJ2hKtcD0dfwKJnwGXC7pK8SFpokVt3MlbZqe78qMmDcRff6lEir/tv13SYtJWsz2OakKZlYg6XXA64GVgbWBRwFHEP36fSQJK1NTWqT1IMq0C2wk6fYR20U38eIiE27157CyfZdxjaBIgqbH/W2SLWLcoHRCauA8tCNxzt+UbqK5fVRrlP4MFx9j4WNYmeVUUdpKpdILkpYB9qZQj2taGV+OaBO4i+4OOoOVxFscNpXbEK0M37B9a7p/ZRcUuJR0le1NWvz/fIKVSsKomc//E0KUsOkqs5ftZ2bGGzW+bJFNSRfZ3irnsRPE+zVlEzTXAm8eaoH5Uu6Faw/Hb1zChyjzz9Y8kXTOJHfb9nYZMU8GNiL0hJpOU1ltTuk9vTMhTvswou1nc9tb5sQrjaSrgScDlw0++2qIGlcWPCok0joUc4EJUKq9uPkNwGOBrgmGnYjP2guJiskBc4Hv2L64Tby+SBVh8+GWYt997a8KiRg34hXZ30a8pk30iXS3iR7EbVZrPK/jtVvxz3DJMfZ1DCuzl1qhUqlU+qJ0j+uDp/6v1pwMbCbpscDRxEXTtwinhdYOQlMlaEir0tOI09fK2GsIV5nPMOYq0zrBJWl74hg9UtLnh8Z3X4fxlbZ1/nOpZEqiSAtMj8evqOaJ7W07jGUiTmP85KQrOwF3A28jzjErEueb2cI9tu8dSGoohHjrStbMUtSmt6eWhsloK25epALE9qnAqSrssFKa3ETCiDh97W8pEWOg3P42KG0T3Ue1Rmmr7dJjLH4MK7ObmlCpVCp9UbpPHUkvBJ6ebp6bu6LT4AHb90naBfis7cMlXdUhXqkEzbrADsBDGO8wMJdYOcnC5Vxl/kjof7yQuDAcMJeY2OayAnAn8JzGti5uCqUTNKVaYPo6fkU1TyR9cNR2d7DjTOeCpYDHpU032v73ZI+ZIl5zAjsbS6rPk/ReYFlJzwb2AU6f4jGVfinqisKCF6Bs1Yraw4S7tMPKbKf0/pYSMS6Kkk00ocO10/CQcr83h6o1vkiZao3SzkZFxtjXMazMfmpCpVKp9EVph5qPA5sDx6dN+0na2va7u4wxXdS8irHERRc3hSIJmtIrYyrsKmP7GuAaSd/qMhkeEbeorTPlEzQbp9/DNsRbprjTaoHp6/hRXvOkmaxYhkjydar4SZVbxwK/ISaGj5b0KtutXH5SomjYTt2D313KyQvzbqL18TpCL+GHwFdndESVoiKtXvAClDNd4VTaJWm2U3p/S4kYl6Yvm+g+qjVKCy2XGmNvVtuV2U3VUKlUKr3Q6HHdkPiyWh74oO0jMuNdC2w8WDVIF69XdRReWw94I3CJ7W8rBF9favvjmfEuAz5L2ETvaPtmSdfbXj8z3qGEi8ldxMrJRsD+tr/ZMs6Otk+XNNLxxXbWyn7q/f4YsB7jV+7WyoxXxNZ5YaGH41dc82Qo/tJEW8NzO8SYQ1y43phuPw74dq4u0GxH0nLA3YML9XTeWtr2nTM7skUXSUcCh7ucC8yodoEf2H5rqfhDz5Wts1To+a9yOGpda3tDhaXzmV3PL7OV0vsr6TnENcJ6wFkkEWPb5xYbdAckrekRNtHD26YRZzvbPx1qW55Hl2qNUp/hvsZY6hhWFh5qhUqlUukF24NV2PNIDjUFeAgwaJtZsWsw2zcQFrOD2zcDWcmUxF5EguYjKZmyJtAq+THEc2y/M1W83ELYHZ/TNqb7c+U5hqjW+AywLbH/XUqXS9k6A+UTND20wBQ9fj1pnjR5EN0/y0sOkikAtv8nTVCySVox69g+RtLDgAfPogvXs4FnAf9Kt5clJlGzQjR3EaWoK0pPLQ2TPmWPsadDaYeV2U7R/bV9VkosD0SM93OPIsYZlLKJ7rNao9RnuK8x9mm1XZmF1IRKpVIpiqS3T3a/7U9nhv4ocKWkc4kvz6cD78mMBUD6Mh7VApM1aewhQTOYaD6fWMX/R8dW6/fQsLKeZNt0Wdb22ZKU+vQPknQB87fETJcits4NiiZoKN8CU/T4lU74SLqOsc/H4sAqdBd8vULS0UQZPUT5fJYLEYCkA4HNCN2hY4CliIRjMbeojixje5BMwfa/FC4flZmjtE1v0ZYGSZ8CjrH98wn+JcuVrSBHSloJeD+hE7Y88IGZHVKvFN1fLXgR42mhwmL4tgffYwePqtbIHmhQSmi56BhLH8PKwkNNqFQqldL04cYD8AKiL/2fwO+Ad9n+v44xN2v8vQwx8V45N1jpBA1wuqRfEi0/+0hahXA0aTuuvlxl7pa0GPArSW8B/gCs2iHe3yS9kvG2zn/vEK9ogsb2Yc3baeLTxbGm9PErnfDZofH3fYRrUpf3C8CbgDcTiUcB5wNf6hBvF2AT4EoA23+U1Nc5KIc7JG060K+R9CTi81yZIUqJtPYoQPlLYhK/BJGs+bbt2xpxW7nP9UBxh5VZTun9XdAixtOlFzF8eqjWKPUZblBqjH0dw8osp2qoVCqVhQJJ2xFlnk8j2g6uBs63/bnCz3Oh7a0zH/vQxs15CRrbIysHphlzJeB22/cnPYYHt00kSdqIEFQ9GGiOZS5wju1/Zo5tc2LC/hDgw0Qb1qG2L82Mtzph67wFY7bO++VePEn6CfB1xido9rJdZIU3vTaX214n8/FFj9+I+J00TyQ9Ffi57bnp9vLAE21fVmJ8JZB0ue0nD3Ql0mfkki7aSiVJr/F3CGcngNUInabsqpzK7EDSh2wfKOmYEXfbHV1vJK1LtAHuDlwEHGV7Mp2kBYKkMxhzWJlXlTOccP5Poa/9HRIxfp5niZC2yonhD6o1DgUOaNy1AnCA7Sd2fY6u9DXGUsewsvBQEyqVSqUXJB1LTIZvTbdXAg7rcpGZLkA2J/Qm3gjcZfvxHeI1VyQWIypW3mR7o9yYI54jO0EzFOdI26/vGGNJl3WVGcRdgZhAZFv09kEPCZqRLTC2v9BxnL0cvwIJn6uATZ0uFFI1zRU5gpiSTrS929AxnEdOAkRREvAB4JFEG9fHgNcA37J9eNt4fZE0YtYlKnJ+2cdnsDJz9CFAmb7rdiASKo8GTiQWFO6w/bIu4+2KOgitL4z0sb9agCLGbZG0DOFM1skmWtJOwM7ACxlfyTkX+I7tizsPtiN9jbHUMawsPNSWn0ql0hcbDpIpALb/KWmT3GCSzgaWAy4BLgA2t/2XjmNsrjDdB9wM7JYbbIIETan2g82m/pcpWUNSSVeZzYiS9Aen27cBr2m7+q7Cts6Nx/2OuFAqRdEWmFLHrxGvtOaJBskUANsPpDaEHPZLv3eY9L9aYNuSdgbeBdxOJC0+aPvHpZ6jEOsy9pnbRBK2vzHDY6qUo2hLg6RPE5PtnwIftX15uusTkm6c+JELjIslbeCCLkmznKL7OwMixm0pYhNt+1Tg1NlcrdHjGBc1a/FFnppQqVQqfbGYpJUG7SSSVqbbOeda4gJ1faL89lZJl9juokewt+2bmhs6iqUVTdAM0TV5BOVdeb4G7GP7ApjntnIMYZXdhsGFxhUdxjKPvhI0RLvGuBYYSV1aYEodvwGlNU9ukrQv8OV0ex/gpkn+f0Js/2kQw/a7mvdJ+gSRFMnhEuBW2wdM+Z8zQBLN3YZIqPyQEFO8EKgJlYWcPgQoU9XVP4GNPNpa+8k5cQtT1CVpIaD0/hYVMe6Bx9reVdJOto+V9C3gzA7xrpL0ZmZ3tUbpMZY+hpVZTk2oVCqVvjiMWNn5LjGp3Y1w6snC9ttgno7DXsRFySOApTuM8buUFUsrnaCZh+3nFQhT2pVn7iAZkMZ4oaTWbSsub+tcNEHT4MuMf7/cOWJbG4ocvwalEz5vBD5PuFuYsADu1HZGtOYMJ0+2H7FtumwLvEHSb2mI8s6iyd1LgI2Aq2zvJenhwFeneExl4aC4AOWg6sr2hye4/7ZR2xcwpV2SZjtF9rdHEePSlLbFXhiqNUqPcVGzFl/kqQmVSqXSC7a/IekKQnRNwIsctsJZKFxQnkYkO35LrO5fMOmDJo7Vl7VdkQSNpNMZUVkxwHZuG0tpV5nLJX2FEH010Q9+7qD1ycnZpAVFbJ17SNDMe3jBFhgof/yKJnxSS10RvQZJbyIqXNaSdG3jrgcTgpu5zPbJ3d3pfXJf0sr5CyGqXVnI6bFd4FJJm9v+WcGYxcjVoFpYKbi/zyDauHYccZ+B2ZJQKW2LvTBUa5Qe46JmLb7IUxMqlUqlFyTtbfto4IbGto/bfndmyGWBTwNzOrYxQOGVxR4SNJ/KeMx02J9YHduXcJXZDnhVh3gbp9/DFS5bEheI200niPqzdS6SoGlQrAUmsXH63en4NSia8FFZYelvAT8ihGOb54C57mADO5snd6l941pJDwGOIlxC/gVcPtnjKgsdpdsFhquu/tNbahYJbA/O8wePEjGegSFNRGmb6IWhWqOPqpxFyVp8kacmVCqVSl+8RNLdto8HkPQlOrTn2P5kqYH1sLJYNEFj+7wCYxoV92cwz61lX3d0lbG9bZGBhaXsFYSAbFOQdS7wtrbBekzQFG2BKXj8BpRO+BQTlk6tCrcRFrBIWpWYfC4vafkkIPwfRWrf2DgdwyMU9qsr2L52iodWFi5KtwvM9qqrSjeKihj3wKmM2UTfUyDewlCtUXqMpY9hZZZTbZMrlUovKGwBTyNac7YH/mF7/xkd1BAqbG1XuvRb0jrEin4vrjzEF34XV5kPjtpuO8tZRoVsnSVtRFR/HAw0xzgXOGcglDzT9HD8ViUSPtsxlvDZ35luWJKuAbYZEpY+z/YGOfFSjB2JSrP/ItpfHgP8wvYTc2POZiR9Efj6bG3fqHRH0lW2N5F0re0NFTbZZ9puW2E2iHec7T2m2lZZuGhUsh4KNEW0VwAOmC3nQBW2iZa0NGPVGkumzc79nuuD0mMsfQwrs59aoVKpVIqSJl0DXgt8n9BIOFjSyl3K+3ug9Mpi6dLv2erKM+COxt/LEFU6XY5fEVtn29cA10j6VokEzYDCLTBQ+PiV1DxJHAZcImnQIrUr8JGOMQ8Bngr8JE1CtyVVrfyHUts3/vMp3S4wbmItaXFmT/VCJZ/iIsY9UdoWe2Go1ig9xkXNWnyRp1aoVCqVoiisBZsnlmYCwLnVFX3Qw8riSUSC5uU0EjS298uMN8f2kyRdN6gKkHSB7adlxrvI9lZTbcslrfKcZvu5mY+/kLEE0o6kBFKj97xtvNIVPlfZ3mSqbbkUOH6lEz5I2hLYDHiA0C/qVIEl6Qrbm6Xql02SzsvltmeDHWxxJD1m1PbZrP1SaYek1xJtHBsAXye1C9j+Sss47wHeS+iFDSyTBdwLHGn7PaXGXJk5ehAxLoqkG4DHAkVsoheGao0eqnKKHsPK7KdWqFQqlaLYXjNpdGxhu4t7x4Kg9MpiaaX42e7KM8yD6OZgUtrWuXSFz2KSVhpqgSn5Pdr1+BXTPAGQtB9RZfY94rh9RdJRtg/vMMZbFdbn5wPHS/oL3XRtZjU1cbJIUESA0vbHgI9J+lhNnvxHU7qStTSlNXwWhmqN0mOsOkiLGDWhUqlUipNWnT8FbDHTY5mC0kJkpRM0+zPelWdbYM8O8TZOv4u4yki6jrFqpMWBVYjKnFxKJ5BKJ2iKtsD0cPxKJ3z2Bp5q+44U7xPAJUCXhMpOwN2E2PArgBXpts+VykxTtF3A9nskPZLQF1qisf38rrErs4LSrcZF6SEJvDXw6lS9PFurNYqOsSbSFz1qQqVSqfTFWZJeDHzPs7e38Ow0+TyfVBmgbvaFpRM0ayQxy38R1RVI2hW4LCdYD64yOzT+vg/4s7tZWu9PWVvnogka29+Q9GvGWmD26li6Xfr4ldY8EXB/4/b9dKvwYZCcSRw74T9WKgsPj7L9vFLBJH2c0EK6gbHPn4nvqcrCT+lK1tnOwlCtsTCMsTKLqRoqlUqlFyTNBZYjLgjvYizjv8KMDqyBpCttbzq0bY7tLAFASWvavnmqbR3HN9+2FvFKu8o8Ffi5k/1yauV4ou2shE8j7goxrG62zpI2J1b+HkIkaFYEDrV9aWa84RaYnYHsFpg+jl9JzRNJbycSWqekTTsTjjWfzYg1l/m1lcwsPC9UKm2QdCRweKl2AUk3Eu17s1XAs9KBgWaUpPMJa/v/Ay6fTfpylUqlHTWhUqlUFjn6si8slaCRtD3wfGA34ISh8a2XK+Ap6R2Nm/NcZXJ7tyVdBWw6qEBK1SBXdEj4FLV1bsQtlaC5ltAGGrTALAdc0kGsr/TxK5rwSTE3JcqhBZxv+6rcWJXKfyI9iHj+CNjV9r/KjbIyWyglYlypVGYPteWnUqn0hqQXAk9PN8+1/YOZHE+DovaFjQTNipJe1LhrBRqicy34I3AF8EKiL785vrdlxAPA9mHN20nn5rTceERSfl5WPmnndPleKWrrPJygkdQ1QVO6Bab08SuueZKEiruKFY8jva7r2D5G0sOAB+dWcVUqs4DS7QJ3AldLOpuGJovtfQs/T2VmKCJiXKlUZg81oVKpVHoh9YFvDhyfNu0naWvb757BYQFg+1Tg1IL2hUUTNLavAa6RdHxHTY2p6Ooqc5OkfYEvp9v7ADd1iDd3kEwBsH1hahXJpWiCJj32MknNFpijO4yv9PErrnlSGkkHEi1J6xLHcyngm0AR6+5KZUHTgwDlaXRLdFdmN0VFjCuVysxTW34qlUovpPaIjW0/kG4vDlw1m5TdJR0KHEJovJwBbATsb/ubmfGKJGgknWh7tyEXmHl0KCUf6Spj+wuZ8VYFPk+Ixxo4mzh+f8mM9xkiydO0df4nUR7d2tZZ0kW2t5pqW8uYxVpgejh+xTRP+kLS1cAmwJW2N0nbrp1N54VKpVLpC0nX215/psdRqVTKURMqlUqlF1JCZRvb/0i3VybafmbNxEnS1bY3lrQLMfl8G3CO7Y0y4xVJ0EhazfafJD1m1P25K6JD8Uq4yhRF0jmT3G3bbW2diyZoFgZmu+ZJQ5DxStubdtWhqVT+00jWraMS6VW09D+A0iLGlUpl5qktP5VKpS8+Clwp6Vxicvd04D0zOqL5WTL9fj7wbdv/kDp1SDzH9jtTguYWwrb2HKKlYdqkZMriwNG2n9VlQEOsxpCrjKRsVxlJxwL72b413V4JOCxX5NblbZ03Tr8PHNq+JTFhaZWgKU3p4wf9aJ6UQvHh+oGkrwAPkfQ64DXAUTM7skplVrFZ4+9liO+RlWdoLJXybA28OiXOOosYVyqVmacmVCqVSl+8gNCw+CfwO+Bdtv9vZoc0H6dL+iVRUbKPpFWAuzvEK5agsX2/pDslrWj7tg5javJloOkgc+eIbW3YcJAMALD9T0mb5A6utK1zDwma0hQ9frMd25a0M/Au4HZCR+WDtn88owOrVGYRtv8+tOmzki4ERp4fKwsdpUWMK5XKDFMTKpVKpS+OIVZiXkgIn14t6Xzbn5vZYY1h+93JCeX2QQID2KlDyNIJmruB6yT9GLijMe5ct4fSrjKLSVrJ9j9hXltXl3h3NP6eZ+ucG6x0gqYHSh+/hYFLgFttHzDlf1YqiyCpbW/AYkTFyoMn+PfKQkYPIsaVSmWGqRoqlUqlN1LbyubAtsAbgbtsP35mRzUaSUfafn2BOCsxlqBZjrCEzarMkfSqUdttHztq+zTifQ84l/GuMtva3jkz3p7Ae4GT0qZdgY/YPi4n3oj4SwOn2X5u5uPf0bg5L0HTpaWmJH0fv9mIpBuAxwG/ZXySsJa7VyrMpyV1H/Ab4FO2b5yZEVUqlUplMmpCpVKp9IKks4HliBXpC4ALc91LFgQDkcyC8YokaEpS2lUmxdySWEF9AJhTyIZ6EHsl4HLb6xSK1ylB0wd9Hr/ZSGmh5UqlUqlUKpWZ5D+9tLhSqcwc1wJPAtYHbgNulXSJ7btmdlgTUjrZs9nU/zI5ktYBPgasR1RYAPluDylx8rKu4xogaT/gtcD3CGG9r0g6yvbhmfFG2jqXGGviQUT72ayg9PFbGKiJk0plciStSAhpPz1tOo+wty+lpVWpVCqVgtQKlUql0iuSlgf2Av4f8AjbS8/wkBYIks6w/byOMS4kLqw/A+xIHEfZHnatmW68oq4yyRp7C9t3pNudLHBL2zpPlKCx/YXcmCUpffwqlcrCj6STgeuBQWvnHsBGtl80c6OqVCqVykTUCpVKpdILkt4CPI2oUvkt4fhzwYwOKiHpdMYm2vNh+4Vdn6NrMiWxrO2zJSmt7B8k6QLmtwGeLqVdZQTc37h9f9qWS1FbZ0IzZUDnBE0PlD5+lUpl4Wdt2y9u3P6QpKtnajCVSqVSmZyaUKlUKn2xLPBpQhdiNk1iAT5VMliPCZq7JS0G/ColqP4ArJoZC8q7yhwDXCbplHR7Z+DoDvFK2zqXTtCUpvTxq1QqCz93Sdra9oUAkrYinOMqlUqlMgupLT+VSqXSEUnPmOx+2+dlxt2csA1+CPBhYAXg0NyEQB+uMsnic2uisuJ821d1iHW17Y2Htl3boYXoKmDTgVV0Sk5dUVJ8uCslj1+lUln4kbQx0e6zYtr0T+DVtq+ZsUFVKpVKZUJqQqVSqSyylBZ9LY2kzYD3AY8Blkyb3UVjYza7yvRg61w0QVOpVCoLCkkrANi+fabHUqlUKpWJWWymB1CpVCozyDHE5P0+YFvgG0CXao11JH1X0g2Sbhr8dBjf8WmMLyb0QHYgxGlzx7cf8BXgoUTr0FckvbXD+ErzRmBLorXpFuApQBfr6Zsk7StpyfSzH9Dl9ahUKpVekfRRSQ+xfbvt2yWtJOmQmR5XpVKpVEZTK1Qqlcoii6Q5tp8k6TrbG6RtF9h+Wma80q48F9reOuexE8RbpFxlJK0KfB7YjtC4ORvYP9lHVyqVyqxD0lW2NxnaduVsalWsVCqVyhhVlLZSqSzKlBZ9Le3Kc6CkrxKJgHsGG21/LzPerHaVKW3rnBInLys3wkqlUumdxSUtbfseAEnLAkvP8JgqlUqlMgE1oVKpVBZl9gceBOxLiL5uC+zZIV7pBM1ewOMJ/ZQH0jYDuQmV2e4qU9TWuXSCplKpVBYA3wTOlnQMcb5/DSFSW6lUKpVZSG35qVQqiyySdrV90lTbWsQr7cozrxWpFLPZVUbSNcA2Q7bO5+UegwlK5+fbVqlUKrMJSc8DnkWcp8+yfeYMD6lSqVQqE1ArVCqVyqLMexizEJ5s23RZw/bPgH8R1SVI2hXISqgAl0paz/YNmY+fD9tXAleWileYw4BLJI2zde4QbzFJKw0laOr3XqVSmdXYPgM4Q9IONZlSqVQqs5taoVKpVBY5JG0PPB/YDTihcdcKwHq2n5wZdz7hwC5igpJ+AawN3ExoqIiOtsmznZK2zpL2BN7LWIJsV+AjtrOdnCqVSmVBUcVoK5VKZfZTV+oqlcqiyB+BK4AXAnMa2+cCb2sbrJGgeaSkzzfuWoGwZM7leR0eu9CRbI1fS2jEiLB1Psr24TnxbH9D0q8ZS9Ds1SVBU6lUKguYWSMaXqlUKpXR1AqVSqWyyCJpCdtdEh6DOBsBGwMHAx9s3DUXOGfQclKZnNK2ziMSNDsD2QmaSqVSWZBIerLty2d6HJVKpVKZmJpQqVQqixySTrS9m6TrCBeFcXSYwBdJ0CyqpNdjc9t3p9vLAD/rIEpbNEFTqVQqfSPpQcA7gNVtv07SOsC6tn8ww0OrVCqVyghqy0+lUlkU2S/93qFEsEGCBrhKUrEEzSJIaVtnAfc3bt9PLaGvVCqzm2OIVtQt0u1bCB2omlCpVCqVWUitUKlUKoskkhYHzrT9rAKxVrP9J0mPGXW/7d92fY5FhZK2zpLeDrwKaCZovm77sx2HWalUKr0g6QrbmzUt3iVdY3ujmR5bpVKpVOanVqhUKpVFEtv3S7pT0oq2b+sY608pQXN0iQTNokxJW2fbn5Z0LmMJmr26JGgqlUplAXCvpGVJ7aiS1iZc3iqVSqUyC6kJlUqlsihzN3CdpB8Ddww22t63baCSCZpKOUomaCqVSmUBcBBwBvBoSccDWwGvnskBVSqVSmViastPpVJZZJH0qlHbbR+bGe9E4KlA5wRNpVKpVBZNJD2U+C4RcKntv83wkCqVSqUyATWhUqlUKoUonaCpVCqVyqKFpNOAbwOnDRzKKpVKpTJ7qQmVSqWyyJLsKD8GrAcsM9hue60ZG1SlUqlUFlkkPQN4KfAC4HLgBOAHAzv5SqVSqcwuakKlUqksski6EDgQ+AywI7AXcV48MDNeTdBUKpVKpTNJ6Hw74HXA82yvMMNDqlQqlcoIFpvpAVQqlcoMsqzts4kkym9tH0RcwOZyDPBl4D5gW+AbwHGdR1mpVCqVRYbk8vNi4I3A5kBtG61UKpVZSk2oVCqVRZm7JS0G/ErSWyTtAqzaIV7pBE2lUqlUFiEknQD8gvju+CKwtu23zuyoKpVKpTIR1Ta5UqksyuwPPAjYF/gwUVWyZ4d44xI0wB/olqCpVCqVyqLFMcDLbd8/0wOpVCqVytRUDZVKpbLIImkz4H3AY4Al02bb3jAz3ubEyuJDiATNCsChti/rPtpKpVKp/KciaTvbP5X0olH32/7egh5TpVKpVKamVqhUKpVFmeOBA4DrgAcKxDOhmdJM0BwFZCVoKpVKpbLI8Azgp4RA+jAGakKlUqlUZiG1QqVSqSyySLrQ9tYF493IiASN7d+Weo5KpVKp/OciaU3bN0+1rVKpVCqzg5pQqVQqiyySngnsDpwN3DPYnltaXTpBU6lUKpVFC0lX2t50aNsc20+aqTFVKpVKZWJqy0+lUlmU2Qt4PNGeM6go6VJafaCkr1IoQVOpVCqVRQNJjweeCKw4pKOyArDMzIyqUqlUKlNREyqVSmVRZiPbGxSMVzpBU6lUKpVFg3WBHQhR86aOylzgdTMxoEqlUqlMTW35qVQqiyySjgI+Y/uGQvGuK5ygqVQqlcoihKQtbF8y0+OoVCqVyvSoCZVKpbLIIukXwNrAzUSLjuhmm1w0QVOpVCqVRQtJywB7E+0/81p9bL9mxgZVqVQqlQmpLT+VSmVR5nmF420NvEpSkQRNpVKpVBY5jgN+CTwXOBh4BfCLGR1RpVKpVCakVqhUKpVKISQ9ZtT2aptcqVQqlekg6Srbm0i61vaGkpYEzrS93UyPrVKpVCrzUytUKpVKpRA1cVKpVCqVjvw7/b5V0vrA/wFrzNxwKpVKpTIZNaFSqVQqlUqlUqnMDo6UtBLwfuA0YHngAzM7pEqlUqlMRG35qVQqlUqlUqlUZgGSlgZeTFSlLJk22/bBMzaoSqVSqUxIrVCpVCqVSqVSqVRmB6cCtwFzCHHzSqVSqcxiaoVKpVKpVCqVSqUyC5B0ve31Z3oclUqlUpkei830ACqVSqVSqVQqlQoAF0vaYKYHUalUKpXpUStUKpVKpVKpVCqVWYCkG4DHAjcTLT8iNFQ2nNGBVSqVSmUkNaFSqVQqlUqlUqnMAiQ9ZtR2279d0GOpVCqVytTUhEqlUqlUKpVKpVKpVCqVSkuqhkqlUqlUKpVKpVKpVCqVSktqQqVSqVQqlUqlUqlUKpVKpSU1oVKpVCqVSqVSqVQqlUql0pKaUKlUKpVKpVKpVCqVSqVSaUlNqFQqlUqlUqlUKpVKpVKptOT/AyBYDLxU0vDAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 9))\n",
    "sns.heatmap(X_train_rfe.corr(), cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_logit_rfe.fit(X_train_rfe, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_rfe = X_test[X_train_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred_rfe = clf_logit_rfe.predict(X_test_rfe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8413793103448276"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(y_test, y_test_pred_rfe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(metrics.confusion_matrix(y_test, y_test_pred_rfe), annot=True, fmt='d')\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.87      0.92      0.90     10192\n",
      "         1.0       0.71      0.58      0.64      3293\n",
      "\n",
      "    accuracy                           0.84     13485\n",
      "   macro avg       0.79      0.75      0.77     13485\n",
      "weighted avg       0.83      0.84      0.84     13485\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, y_test_pred_rfe))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8986877848735795\n"
     ]
    }
   ],
   "source": [
    "prob_rfe = clf_logit_rfe.predict_proba(X_test_rfe)\n",
    "prob_rfe = prob_rfe[:, 1]\n",
    "roc_auc_rfe = metrics.roc_auc_score(y_test, prob_rfe)\n",
    "print(roc_auc_rfe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ploting roc curve\n",
    "fpr_rfe, tpr_rfe, thresholds_rfe = metrics.roc_curve(y_test, prob_rfe)\n",
    "\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "\n",
    "# Plot the roc curve for the model\n",
    "plt.plot(fpr_rfe, tpr_rfe, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Principal Component Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31462, 80)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>workclass_Local-gov</th>\n",
       "      <th>workclass_Private</th>\n",
       "      <th>workclass_Self-emp-inc</th>\n",
       "      <th>workclass_Self-emp-not-inc</th>\n",
       "      <th>workclass_State-gov</th>\n",
       "      <th>...</th>\n",
       "      <th>native-country_Portugal</th>\n",
       "      <th>native-country_Puerto-Rico</th>\n",
       "      <th>native-country_Scotland</th>\n",
       "      <th>native-country_South</th>\n",
       "      <th>native-country_Taiwan</th>\n",
       "      <th>native-country_Thailand</th>\n",
       "      <th>native-country_Trinadad&amp;Tobago</th>\n",
       "      <th>native-country_United-States</th>\n",
       "      <th>native-country_Vietnam</th>\n",
       "      <th>native-country_Yugoslavia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14479</th>\n",
       "      <td>-1.558902</td>\n",
       "      <td>-0.433575</td>\n",
       "      <td>-0.23252</td>\n",
       "      <td>-0.219315</td>\n",
       "      <td>-0.074097</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14944</th>\n",
       "      <td>0.346388</td>\n",
       "      <td>1.531844</td>\n",
       "      <td>-0.23252</td>\n",
       "      <td>4.467376</td>\n",
       "      <td>0.760953</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9772</th>\n",
       "      <td>-0.949209</td>\n",
       "      <td>-0.433575</td>\n",
       "      <td>-0.23252</td>\n",
       "      <td>-0.219315</td>\n",
       "      <td>0.343428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39549</th>\n",
       "      <td>0.803658</td>\n",
       "      <td>-0.433575</td>\n",
       "      <td>-0.23252</td>\n",
       "      <td>4.467376</td>\n",
       "      <td>-0.074097</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30631</th>\n",
       "      <td>0.270177</td>\n",
       "      <td>1.531844</td>\n",
       "      <td>-0.23252</td>\n",
       "      <td>-0.219315</td>\n",
       "      <td>1.596002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            age  educational-num  capital-gain  capital-loss  hours-per-week  \\\n",
       "14479 -1.558902        -0.433575      -0.23252     -0.219315       -0.074097   \n",
       "14944  0.346388         1.531844      -0.23252      4.467376        0.760953   \n",
       "9772  -0.949209        -0.433575      -0.23252     -0.219315        0.343428   \n",
       "39549  0.803658        -0.433575      -0.23252      4.467376       -0.074097   \n",
       "30631  0.270177         1.531844      -0.23252     -0.219315        1.596002   \n",
       "\n",
       "       workclass_Local-gov  workclass_Private  workclass_Self-emp-inc  \\\n",
       "14479                  0.0                1.0                     0.0   \n",
       "14944                  1.0                0.0                     0.0   \n",
       "9772                   0.0                1.0                     0.0   \n",
       "39549                  0.0                1.0                     0.0   \n",
       "30631                  0.0                0.0                     1.0   \n",
       "\n",
       "       workclass_Self-emp-not-inc  workclass_State-gov  ...  \\\n",
       "14479                         0.0                  0.0  ...   \n",
       "14944                         0.0                  0.0  ...   \n",
       "9772                          0.0                  0.0  ...   \n",
       "39549                         0.0                  0.0  ...   \n",
       "30631                         0.0                  0.0  ...   \n",
       "\n",
       "       native-country_Portugal  native-country_Puerto-Rico  \\\n",
       "14479                      0.0                         0.0   \n",
       "14944                      0.0                         0.0   \n",
       "9772                       0.0                         0.0   \n",
       "39549                      0.0                         0.0   \n",
       "30631                      0.0                         0.0   \n",
       "\n",
       "       native-country_Scotland  native-country_South  native-country_Taiwan  \\\n",
       "14479                      0.0                   0.0                    0.0   \n",
       "14944                      0.0                   0.0                    0.0   \n",
       "9772                       0.0                   0.0                    0.0   \n",
       "39549                      0.0                   0.0                    0.0   \n",
       "30631                      0.0                   0.0                    0.0   \n",
       "\n",
       "       native-country_Thailand  native-country_Trinadad&Tobago  \\\n",
       "14479                      0.0                             0.0   \n",
       "14944                      0.0                             0.0   \n",
       "9772                       0.0                             0.0   \n",
       "39549                      0.0                             0.0   \n",
       "30631                      0.0                             0.0   \n",
       "\n",
       "       native-country_United-States  native-country_Vietnam  \\\n",
       "14479                           1.0                     0.0   \n",
       "14944                           1.0                     0.0   \n",
       "9772                            1.0                     0.0   \n",
       "39549                           1.0                     0.0   \n",
       "30631                           1.0                     0.0   \n",
       "\n",
       "       native-country_Yugoslavia  \n",
       "14479                        0.0  \n",
       "14944                        0.0  \n",
       "9772                         0.0  \n",
       "39549                        0.0  \n",
       "30631                        0.0  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=None, random_state=0,\n",
       "    svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 8))\n",
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Number of Components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.18774128, 0.32350373, 0.4561854 , 0.57176384, 0.67386342,\n",
       "       0.72483059, 0.75344527, 0.78163552, 0.80405243, 0.8239841 ,\n",
       "       0.84047984, 0.85589486, 0.87067588, 0.88327898, 0.89491854,\n",
       "       0.90650573, 0.91572894, 0.92403235, 0.93194527, 0.9389754 ,\n",
       "       0.94547719, 0.95141761, 0.95607701, 0.96052032, 0.96488875,\n",
       "       0.96903919, 0.97280873, 0.97641379, 0.9797012 , 0.98226534,\n",
       "       0.984807  , 0.98655825, 0.98793924, 0.98924335, 0.99034352,\n",
       "       0.99105057, 0.99172451, 0.99230608, 0.99281903, 0.99326887,\n",
       "       0.99369656, 0.9940962 , 0.99448669, 0.99484207, 0.99514386,\n",
       "       0.99544374, 0.99572863, 0.9960106 , 0.99627388, 0.99653615,\n",
       "       0.99679056, 0.99704161, 0.99727481, 0.99749991, 0.99770808,\n",
       "       0.99790739, 0.99809354, 0.99826785, 0.9984231 , 0.9985675 ,\n",
       "       0.9987006 , 0.99882389, 0.99894353, 0.99906084, 0.99917425,\n",
       "       0.99926249, 0.99934604, 0.99942482, 0.99949936, 0.99957301,\n",
       "       0.99964028, 0.99970456, 0.99976535, 0.99982439, 0.99988095,\n",
       "       0.99992581, 0.99996254, 0.99999438, 0.99999855, 1.        ])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca_final = PCA(n_components = 15, random_state = 0)\n",
    "\n",
    "X_train_pca = pca_final.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31462, 15)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrmat = np.corrcoef(X_train_pca.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29d634ede08>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g4uaakGpIAQAAJq5m2pG6ZBcAAIBRSEgBAAAmrmYaJc50WAAAAEzdtglpVT09yR3d/emq2pPk6iRPTfLhJD/Z3X95EmoEAADY0WY6hXRpQvq6JJ9ZPH91kkcleeVi3+tXWBcAAAAzt6wh3dXd9y+eX9jdP9zd/6G79yf5yq3eVFV7q+pAVR1YW7t+sGIBAAB2oqrVPsaybFGj26rqyu5+fZIPVtWF3X2gqp6Y5HNbvam715KsrW/d1UMVCwAAwHwsa0i/N8mrq+qfJvlEkt+vqnuT3Ls4BgAAwIrNdQ7ptg3pYtGif1BVj8z6JbqnJTnU3X9+MooDAABgvo7qPqTd/VdJPrjiWgAAADiCXTNNSN2HFAAAgFEcVUIKAADAeOY6h1RCCgAAwCgkpAAAABMnIQUAAIABSUgBAAAmrma6zK6EFAAAgFFISAEAACbOHFIAAAAYkIQUAABg4uaakGpIAVbo8MH9Y5ewEnvO3Td2CYOb62cFwDzMtSF1yS4AAACjkJACAABM3Ezv+iIhBQAAYBwSUgAAgIkzhxQAAAAGJCEFAACYuJpplDjTYQEAADB1ElIAAICJM4cUAAAABiQhBQAAmLiaaUQqIQUAAGAUElIAAICJm2lAKiEFAABgHBJSAACAiduRCWlV/VBVnXOyigEAAGDnWHbJ7o8n+YOq+vdV9QNV9WUnoygAAAD+RtVqH2NZ1pDek+TsrDemT0vy4ap6e1W9qKoeudWbqmpvVR2oqgNra9cPWC4AAABzsWwOaXf3A0nemeSdVXV6kkuTvDDJzyQ5YmLa3WtJ1ta37uqhigUAANiJds10DumyhvQhw+7uzyW5IckNVbVnZVUBAAAwe8sa0hdsdaC7Dw9cCwAAAEcw14R02zmk3X3XySoEAACAncV9SAEAACZuV81zaR4NKQAAwMTtyEt2AQAAYFUkpAAAABM31yRxruMCAABg4iSkAAAAEzfXRY0kpAAAAIxCQgoAADBxVtkFAACAAUlIAQAAJm6uSeJcxwUAAMDESUgBOGaHD+4fu4TB7Tl339glrMQcPyuAncgcUgAAABiQhBQAAGDiyn1IAQAAYDgSUgAAgIkzhxQAAAAGJCEFAACYuLkmiXMdFwAAABMnIQUAAJi4XVbZBQAAgOFISAEAACZurqvsakgBAAAmbq6Xts51XAAAAEychBQAAGDi5nrJroQUAACAUWybkFbVFyW5PMl93f3vquqKJH8nyR1J1rr7cyehRgAAgB1trrd9WXbJ7usXr3l4Vb0oyRlJ3pLkOUkuSvKi1ZYHAADAXC1rSP92dz+pqk5L8rEkX9Hdn6+qf5Pkg1u9qar2JtmbJL/4i/88e/e+YLCCAQAAdpq5ziFd1pDuWly2+4gkD0/yqCSfTPKwJKdv9abuXkuytr511zyzZQAAAE7Isob0l5J8JMnuJK9I8utVdU+SZyS5bsW1AQAAkPmuRrttQ9rdr6qq6xfP76uqNyT5liT/T3e//2QUCAAAwDwtvQ9pd9+34fl/TfKmlVYEAADAQ8x1ld25Jr8AAABM3NKEFAAAgHHNdZVdCSkAAACjkJACAABMnIQUAAAABiQhBQAAmLi5JolzHRcAAAATJyEFAACYOPchBQAAYMeqqkuq6s6quruqrt7mdd9YVZ+vqr+37JwSUgAAgIkbe5Xdqtqd5DVJnpvkUJKbq+qG7v7wEV73yiTvOJrzSkgBAAAmbteKH0fhoiR3d/c93f3ZJNcluewIr/vBJG9O8vGjOamEFACSHD64f+wSVmLPufvGLmFwc/2sAMZUVXuT7N2wa6271zZsn5Xk3g3bh5I8fdM5zkryXUmeneQbj+b7akgBAAAmbtWX7C6az7VtXnKkCjavtPQvk7y8uz9fdXQFa0gBAABY5lCSczZsn53kvk2vuTDJdYtm9LFJvq2q7u/uf7vVSTWkAAAAE1fj3/bl5iTnV9V5ST6W5PIkV2x8QXef9+Dzqro2yVu3a0YTDSkAAABLdPf9VfXSrK+euzvJ67r79qp68eL4NcdzXg0pAADAxI1925ck6e4bk9y4ad8RG9Hu/gdHc063fQEAAGAUElIAAICJm2uSONdxAQAAMHESUgAAgInbNf4quyshIQUAAGAUElIAAICJm8Iqu6sgIQUAAGAUElIAAICJm2tCurQhraqvSvJdSc5Jcn+SjyZ5Y3f/5YprAwAAYMa2vWS3qn4oyTVJvjjJNybZk/XG9Per6lkrrw4AAIDsXvFjLMvmkH5fkku6+/9O8i1JLujuVyS5JMmrtnpTVe2tqgNVdWBt7frhqgUAAGA2jmYO6WlJPp/kYUkemSTdfbCqTt/qDd29lmRtfeuued4wBwAA4CSZ631IlzWkr01yc1W9L8kzk7wySarqy5J8csW1AQAAMGPbNqTd/eqq+ndJvi7Jz3b3Rxb7/yLrDSoAAAArtmNX2e3u25PcfhJqAQAA4Ajm2pAuW9QIAAAAVuJoFjUCAABgRLslpAAAADAcCSkAAMDEmUMKAAAAA5KQAgAATNyu6rFLWAkJKQAAAKOQkAIAAEycOaQAAAAwIAkpAADAxO0eu4AVkZACAAAwCgkpAMzY4YP7xy5hcHvO3Td2CSsxx88KGI45pAAAADAgCSkAAMDEuQ8pAAAADEhCCgAAMHG7zSEFAACA4UhIAQAAJs4quwAAADAgCSkAAMDEzTUh1ZACAABM3FwbUpfsAgAAMAoJKQAAwMTtrh67hJWQkAIAADAKCSkAAMDEzTVJnOu4AAAAmDgJKQAAwMRZZRcAAAAGtG1DWlWPqqqfqqqPVNV/WTzuWOx79Dbv21tVB6rqwNra9cNXDQAAsIPsqtU+xrLskt1fS/K7SZ7V3X+WJFX15UlelOTXkzz3SG/q7rUka+tbd81zfWIAAABOyLKG9And/cqNOxaN6Sur6h+uriwAAAAetFPvQ/onVfUjVXXmgzuq6syqenmSe1dbGgAAAHO2rCF9QZLHJHl3VX2yqj6Z5KYkX5rk+SuuDQAAgOzQOaTd/akkL188HqKqrkzy+hXVBQAAwMydyG1f9g9WBQAAAFvakQlpVd261aEkZ25xDAAAAJZatsrumUkuTvKpTfsryXtXUhEAAAAPMWaKuUrLGtK3Jjmju2/ZfKCqblpJRQAAAOwIyxY1umqbY1cMXw4AAACb7Z5pQnoiixoBAADAcVt2yS4AAAAj21U9dgkrISEFAABgFBJSAACAiZtrkqghBQAAmLidetsXAIBJOXxw/9glrMSec/eNXcJKzPXzAoahIQUAAJg4t30BAACAAUlIAQAAJs5tXwAAAGBAElIAAICJm+squxJSAAAARiEhBQAAmDgJKQAAAAxIQgoAADBxc00S5zouAAAAJk5CCgAAMHFlDikAAAAMR0IKAAAwcTMNSCWkAAAAjENCCgAAMHHmkAIAAMCAjrshrarf3ubY3qo6UFUH1tauP95vAQAAQNYbt1U+xrLtJbtV9dStDiV5ylbv6+61JGvrW3f1cdYGAADAjC2bQ3pzknfnyIs6PXr4cgAAANisap4537KG9I4k39/dH918oKruXU1JAAAAbDTTNY2WXi78z7Z5zQ8OWwoAAAA7ybYJaXe/aZvDXzJwLQAAAByB2758of2DVQEAAMCOs2yV3Vu3OpTkzOHLAQAAYLOZBqRLFzU6M8nFST61aX8lee9KKgIAAGBHWNaQvjXJGd19y+YDVXXTSioCAADgIXbNNCJdtqjRVdscu2L4cgAAANgpliWkAAAAjGymAekJrbILAAAAx01DCgAAMHFVq30cXQ11SVXdWVV3V9XVRzj+3VV16+Lx3qp68rJzakgBAADYVlXtTvKaJJcmuSDJC6vqgk0v+6Mk/0t3PynJjydZW3Zec0gBACbg8MH9Y5ewEnvO3Td2CYOb62fFtE1gDulFSe7u7nuSpKquS3JZkg8/+ILu3nhr0PclOXvZSSWkAAAAO1xV7a2qAxseeze95Kwk927YPrTYt5Wrkvz2su8rIQUAAJi4VSek3b2W7S+xPVIJfcQXVn1z1hvSv7vs+2pIAQAAWOZQknM2bJ+d5L7NL6qqJyV5bZJLu/u/LDuphhQAAGDido0/ifTmJOdX1XlJPpbk8iRXbHxBVZ2b5C1J/n5333U0J9WQAgAAsK3uvr+qXprkHUl2J3ldd99eVS9eHL8myY8leUySn6/1e8nc390XbndeDSkAAMDEjR+QJt19Y5IbN+27ZsPz703yvcdyTqvsAgAAMAoJKQAAwMRVHXFB21OehhQAAGDipnDJ7iq4ZBcAAIBRSEgBAAAmrmYakUpIAQAAGIWEFAAAYOLmmiTOdVwAAABMnIQUAABg4swhBQAAgAGtpCGtqr1VdaCqDqytXb+KbwEAALBj1IofY9n2kt2q+ltJfjTJ2Ul+u7t/dcOxn+/uHzjS+7p7Lcna+tZdPVSxAAAAzMeyhPT1WW+Y35zk8qp6c1U9bHHsGSutDAAAgCTrc0hX+RjLsob0q7r76u7+t939vCQfSPK7VfWYk1AbAAAAM7Zsld2HVdWu7n4gSbr7J6rqUJL3JDlj5dUBAAAw6jzPVVqWkP5Wkmdv3NHdv5zkHyf57KqKAgAAYP62TUi7+0e22P/2qvrJ1ZQEAADARrtmGpGeyG1f9g9WBQAAADvOstu+3LrVoSRnDl8OAAAAm800IF26qNGZSS5O8qlN+yvJe1dSEQAAADvCsob0rUnO6O5bNh+oqptWUhEAAAAPUdVjl7ASyxY1umqbY1cMXw4AAAA7xbKEFAAAgJHt1DmkAAAAjKxm2pGeyG1fAAAA4LhJSAEAACZupgGphhQAgNU5fHD/2CUMbs+5+8YuYSXm+FkxfRpSAACAiZvrXMu5jgsAAICJk5ACAABMnFV2AQAAYEASUgAAgMmbZ0QqIQUAAGAUElIAAICJKwkpAAAADEdCCgAAMHFV88wS5zkqAAAAJk9CCgAAMHnmkAIAAMBgJKQAAAATZ5VdAAAAGJCEFAAAYPIkpAAAADCYbRvSqvryqvqFqnpNVT2mqv5ZVX2oqn6tqh6/zfv2VtWBqjqwtnb98FUDAADsIFW7VvoYy7JLdq9N8rYkj0jye0l+Jcm3J7ksyTWLr1+gu9eSrK1v3dWDVAoAAMCsLGtIz+zun0uSqvqB7n7lYv/PVdVVqy0NAACAdfOcQ7qsId2Y3b5h07HdA9cCAADAEezU2778ZlWdkSTd/U8f3FlVX53kzlUWBgAAwLxtm5B2949tsf/uqnrbakoCAABgo52akG5n/2BVAAAAsONsm5BW1a1bHUpy5vDlAAAA8IXGuzXLKi1dZTfJxUk+tWl/JXnvSioCAABgR1jWkL41yRndfcvmA1V100oqAgAA4CGq5jmHdNmiRlvea7S7rxi+HAAAAHaKZQkpAAAAo5tnQjrPmbEAAABMnoQUAABg4tyHFAAAAAYkIQUAAJi8eWaJGlIAADgGhw/uH7uEldhz7r6xS1iJwwffOHYJbENDCgAAMHHmkAIAAMCAJKQAAAATVyUhBQAAgMFISAEAACZPQgoAAACDkZACAABMXM00S9SQAgAATJ5LdgEAAGAwElIAAICJc9sXAAAAGJCEFAAAYPIkpAAAADAYCSkAAMDEzfW2L/McFQAAAJN3zA1pVT3uKF6zt6oOVNWBtbXrj68yAAAAFmrFj3Fse8luVX3p5l1J3l9V35CkuvuTR3pfd68lWVvfuqtPvEwAAADmZtkc0k8k+ZNN+85K8oEkneQrV1EUAAAAf6N26Cq7P5LkziTP6+7zuvu8JIcWzzWjAAAAHLdtE9Lu/pmqui7Jq6rq3iT7sp6MAgAAcJJU7cyENN19qLufn+T3krwrycNXXhUAAACzd9Sr7Hb3byX55iTfkiRVdeWqigIAAGCjXSt+jOOYvnN3H+7u2xab+1dQDwAAADvEstu+3LrVoSRnDl8OAAAAm811ld1lt305M8nFST61aX8lee9KKgIAAGBHWNaQvjXJGd19y+YDVXXTSioCAABgkx2YkHb3Vdscu2L4cgAAANgpliWkAAAAjGzH3ocUAACAsY1/25equqSq7qyqu6vq6iMcr6r6V4vjt1bVU49mVAAAALClqtqd5DVJLk1yQZIXVrAyVjsAAAhmSURBVNUFm152aZLzF4+9SX5h2Xk1pAAAABNXK/7nKFyU5O7uvqe7P5vkuiSXbXrNZUne0Ovel+TRVfX47U6qIQUAAGCZs5Lcu2H70GLfsb7mIU7CokZPPGmzb6tqb3evnazvd7LMcVxzHFMyz3HNcUzJPMc1xzElxnUqmeOYknmOa45jSuY5rpM5psMH33gyvk2SeX5Wq7favqqq9mb9MtsHrW36jI70/XvzaY7iNQ8xt4R07/KXnJLmOK45jimZ57jmOKZknuOa45gS4zqVzHFMyTzHNccxJfMc1xzHlMx3XKes7l7r7gs3PDb/wuBQknM2bJ+d5L7jeM1DzK0hBQAAYHg3Jzm/qs6rqi9KcnmSGza95oYk37NYbfcZSf6yu/90u5O6DykAAADb6u77q+qlSd6RZHeS13X37VX14sXxa5LcmOTbktyd5DNJrlx23rk1pHO9Dn2O45rjmJJ5jmuOY0rmOa45jikxrlPJHMeUzHNccxxTMs9xzXFMyXzHNWvdfWPWm86N+67Z8LyTvORYzlnr7wEAAICTyxxSAAAARjGLhrSqLqmqO6vq7qq6eux6hlBVr6uqj1fVbWPXMqSqOqeqfq+q7qiq26vqZWPXdKKq6our6v1V9cHFmPaPXdOQqmp3Vf2nqnrr2LUMoar+uKo+VFW3VNWBsesZSlU9uqreVFUfWfz/9T+NXdOJqqqvWXxODz4+XVU/PHZdJ6qq/tHiz4rbquqNVfXFY9c0hKp62WJMt5+qn9OR/u6tqi+tqndV1UcXX79kzBqPxxbjev7is3qgqi4cs77jtcW4fnrx5+CtVfUbVfXoMWs8VluM6ccX47mlqt5ZVV8xZo3HY7ufa6vqn1RVV9Vjx6iN8Z3yDWlV7U7ymiSXJrkgyQur6oJxqxrEtUkuGbuIFbg/yT/u7q9L8owkL5nB5/Xfkzy7u5+c5ClJLlmsKjYXL0tyx9hFDOybu/sp3X1K/hC2hVcneXt3f22SJ2cGn1l337n4nJ6S5GlZXxzhN0Yu64RU1VlJfijJhd399VlfFOLycas6cVX19Um+L8lFWf/v7zuq6vxxqzou1+YL/+69OsnvdPf5SX5nsX2quTZfOK7bkvyvSd5z0qsZzrX5wnG9K8nXd/eTktyV5EdPdlEn6Np84Zh+uruftPiz8K1JfuykV3Xirs0Rfq6tqnOSPDfJwZNdENNxyjekWf/L7+7uvqe7P5vkuiSXjVzTCevu9yT55Nh1DK27/7S7P7B4/ldZ/6H5rHGrOjG97r8tNk9fPGYxObuqzk7y7UleO3YtbK2q/laSZyb5pSTp7s92938dt6rBPSfJf+7uPxm7kAGclmRPVZ2W5OFZcn+2U8TXJXlfd3+mu+9P8u4k3zVyTcdsi797L0vyy4vnv5zkO09qUQM40ri6+47uvnOkkgaxxbjeufhvMEnel/V7IJ4ythjTpzdsPiKn4M8Y2/xc+6okP5JTcEwMZw4N6VlJ7t2wfSineIOzU1TVE5J8Q5I/GLeSE7e4rPWWJB9P8q7uPuXHtPAvs/4XxQNjFzKgTvLOqvrDqprLTbm/MslfJHn94vLq11bVI8YuamCXJ3nj2EWcqO7+WJKfyXoa8KdZvz/bO8etahC3JXlmVT2mqh6e9SX/z1nynlPFmQ/eQ2/x9XEj18PR+4dJfnvsIoZQVT9RVfcm+e6cmgnpF6iq5yX5WHd/cOxaGNccGtI6wj6/ZZm4qjojyZuT/PCm3/ydkrr784tLac5OctHi8rVTWlV9R5KPd/cfjl3LwL6pu5+a9cv8X1JVzxy7oAGcluSpSX6hu78hyV/n1Lys8IgWN99+XpJfH7uWE7WYf3hZkvOSfEWSR1TV/z5uVSeuu+9I8sqsXy759iQfzPoUDRhFVb0i6/8N/srYtQyhu1/R3edkfTwvHbueE7X4xdUrMpPmmhMzh4b0UB76W9izM4/Ln2arqk7PejP6K939lrHrGdLiMsmbMo/5v9+U5HlV9cdZvxT+2VX1b8Yt6cR1932Lrx/P+nzEi8ataBCHkhzakMy/KesN6lxcmuQD3f3nYxcygG9J8kfd/Rfd/bkkb0nyd0auaRDd/Uvd/dTufmbWL8376Ng1DeTPq+rxSbL4+vGR62GJqnpRku9I8t09v/sb/mqS/23sIgbwVVn/xdwHFz9nnJ3kA1X15aNWxSjm0JDenOT8qjpv8Vv0y5PcMHJNbKGqKuvz3O7o7p8du54hVNWXPbiKX1XtyfoPnB8Zt6oT190/2t1nd/cTsv7/1e929ymd5FTVI6rqkQ8+T/KtWb/U8JTW3X+W5N6q+prFruck+fCIJQ3thZnB5boLB5M8o6oevvjz8DmZwQJUSVJVj1t8PTfri+XM5TO7IcmLFs9flOQ3R6yFJarqkiQvT/K87v7M2PUMYdMCYc/LPH7G+FB3P667n7D4OeNQkqcu/j5jhzlt7AJOVHffX1UvTfKOrK9W+Lruvn3ksk5YVb0xybOSPLaqDiXZ192/NG5Vg/imJH8/yYcWcy6T5P/s7htHrOlEPT7JLy9WfN6V5Ne6exa3SJmhM5P8xnofkNOS/Gp3v33ckgbzg0l+ZfGLuXuSXDlyPYNYXNb13CTfP3YtQ+juP6iqNyX5QNYvJ/xPSdbGrWowb66qxyT5XJKXdPenxi7oWB3p794kP5Xk16rqqqz/QuH541V4fLYY1yeT/FySL0vytqq6pbsvHq/KY7fFuH40ycOSvGvxZ/37uvvFoxV5jLYY07ctfuH4QJI/SXLKjOdBM/65lgHU/K5kAAAA4FQwh0t2AQAAOAVpSAEAABiFhhQAAIBRaEgBAAAYhYYUAACAUWhIAQAAGIWGFAAAgFFoSAEAABjF/weLP0UO6llbVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 9))\n",
    "sns.heatmap(corrmat, cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>workclass_Local-gov</th>\n",
       "      <th>workclass_Private</th>\n",
       "      <th>workclass_Self-emp-inc</th>\n",
       "      <th>workclass_Self-emp-not-inc</th>\n",
       "      <th>workclass_State-gov</th>\n",
       "      <th>...</th>\n",
       "      <th>native-country_Portugal</th>\n",
       "      <th>native-country_Puerto-Rico</th>\n",
       "      <th>native-country_Scotland</th>\n",
       "      <th>native-country_South</th>\n",
       "      <th>native-country_Taiwan</th>\n",
       "      <th>native-country_Thailand</th>\n",
       "      <th>native-country_Trinadad&amp;Tobago</th>\n",
       "      <th>native-country_United-States</th>\n",
       "      <th>native-country_Vietnam</th>\n",
       "      <th>native-country_Yugoslavia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22251</th>\n",
       "      <td>-1.635113</td>\n",
       "      <td>-1.612827</td>\n",
       "      <td>-0.23252</td>\n",
       "      <td>-0.219315</td>\n",
       "      <td>-2.579245</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15880</th>\n",
       "      <td>0.193965</td>\n",
       "      <td>0.352592</td>\n",
       "      <td>-0.23252</td>\n",
       "      <td>-0.219315</td>\n",
       "      <td>-0.074097</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21910</th>\n",
       "      <td>0.270177</td>\n",
       "      <td>0.352592</td>\n",
       "      <td>-0.23252</td>\n",
       "      <td>-0.219315</td>\n",
       "      <td>0.760953</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28131</th>\n",
       "      <td>-1.254055</td>\n",
       "      <td>-1.612827</td>\n",
       "      <td>-0.23252</td>\n",
       "      <td>4.122404</td>\n",
       "      <td>-1.326671</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27660</th>\n",
       "      <td>0.193965</td>\n",
       "      <td>-0.040492</td>\n",
       "      <td>-0.23252</td>\n",
       "      <td>-0.219315</td>\n",
       "      <td>0.760953</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            age  educational-num  capital-gain  capital-loss  hours-per-week  \\\n",
       "22251 -1.635113        -1.612827      -0.23252     -0.219315       -2.579245   \n",
       "15880  0.193965         0.352592      -0.23252     -0.219315       -0.074097   \n",
       "21910  0.270177         0.352592      -0.23252     -0.219315        0.760953   \n",
       "28131 -1.254055        -1.612827      -0.23252      4.122404       -1.326671   \n",
       "27660  0.193965        -0.040492      -0.23252     -0.219315        0.760953   \n",
       "\n",
       "       workclass_Local-gov  workclass_Private  workclass_Self-emp-inc  \\\n",
       "22251                  0.0                1.0                     0.0   \n",
       "15880                  0.0                1.0                     0.0   \n",
       "21910                  0.0                0.0                     0.0   \n",
       "28131                  0.0                1.0                     0.0   \n",
       "27660                  0.0                0.0                     0.0   \n",
       "\n",
       "       workclass_Self-emp-not-inc  workclass_State-gov  ...  \\\n",
       "22251                         0.0                  0.0  ...   \n",
       "15880                         0.0                  0.0  ...   \n",
       "21910                         1.0                  0.0  ...   \n",
       "28131                         0.0                  0.0  ...   \n",
       "27660                         1.0                  0.0  ...   \n",
       "\n",
       "       native-country_Portugal  native-country_Puerto-Rico  \\\n",
       "22251                      0.0                         0.0   \n",
       "15880                      0.0                         0.0   \n",
       "21910                      0.0                         0.0   \n",
       "28131                      0.0                         0.0   \n",
       "27660                      0.0                         0.0   \n",
       "\n",
       "       native-country_Scotland  native-country_South  native-country_Taiwan  \\\n",
       "22251                      0.0                   0.0                    0.0   \n",
       "15880                      0.0                   0.0                    0.0   \n",
       "21910                      0.0                   0.0                    0.0   \n",
       "28131                      0.0                   0.0                    0.0   \n",
       "27660                      0.0                   0.0                    0.0   \n",
       "\n",
       "       native-country_Thailand  native-country_Trinadad&Tobago  \\\n",
       "22251                      0.0                             0.0   \n",
       "15880                      0.0                             0.0   \n",
       "21910                      0.0                             0.0   \n",
       "28131                      0.0                             0.0   \n",
       "27660                      0.0                             0.0   \n",
       "\n",
       "       native-country_United-States  native-country_Vietnam  \\\n",
       "22251                           1.0                     0.0   \n",
       "15880                           1.0                     0.0   \n",
       "21910                           1.0                     0.0   \n",
       "28131                           1.0                     0.0   \n",
       "27660                           1.0                     0.0   \n",
       "\n",
       "       native-country_Yugoslavia  \n",
       "22251                        0.0  \n",
       "15880                        0.0  \n",
       "21910                        0.0  \n",
       "28131                        0.0  \n",
       "27660                        0.0  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_pca = pca_final.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_logit_pca = LogisticRegression()\n",
    "clf_tree_pca = DecisionTreeClassifier()\n",
    "clf_svm_pca = SVC(probability = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifiers = ['LogisticRegression', 'DecisionTreeClassifier', 'SVC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {clf_logit_pca: 'LogisticRegression', \n",
    "          clf_tree_pca: 'DecisionTreeClassifier', \n",
    "          clf_svm_pca: 'SVC'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(algo, name, X_train_pca, y_train, X_test_pca, y_test):\n",
    "    algo.fit(X_train_pca, y_train)\n",
    "    y_test_pred_pca = algo.predict(X_test_pca)\n",
    "    return y_test_pred_pca\n",
    "\n",
    "\n",
    "def performance(algo, name, X_test, y_test, y_test_pred_pca):\n",
    "    # Accuracy Score\n",
    "    score = metrics.accuracy_score(y_test, y_test_pred_pca)\n",
    "    print('Accuracy score for', name, ':', score)\n",
    "    \n",
    "    # Confusion Metrics\n",
    "    sns.heatmap(metrics.confusion_matrix(y_test, y_test_pred_pca), annot=True, fmt='d')\n",
    "    plt.title(\"Confusion Matrix\")\n",
    "    plt.show()\n",
    "    \n",
    "    # Classification Report\n",
    "    print(metrics.classification_report(y_test, y_test_pred_pca))\n",
    "    \n",
    "    # ROC AUC Score\n",
    "    prob_pca = algo.predict_proba(X_test_pca)\n",
    "    prob_pca = prob_pca[:, 1]\n",
    "    roc_auc_pca = metrics.roc_auc_score(y_test, prob_pca)\n",
    "    print(roc_auc_pca)\n",
    "    \n",
    "    # Plotting ROC Curve\n",
    "    fpr_pca, tpr_pca, thresholds_pca = metrics.roc_curve(y_test, prob_pca)\n",
    "    plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "    plt.plot(fpr_pca, tpr_pca, marker='.')\n",
    "    plt.show()\n",
    "    \n",
    "    return roc_auc_pca\n",
    "\n",
    "\n",
    "def main(models):\n",
    "    auc = []\n",
    "    for algo, name in models.items():\n",
    "        print('#'*40, name, '#'*40)\n",
    "        y_test_pred_pca = train(algo, name, X_train_pca, y_train, X_test_pca, y_test)\n",
    "        roc_auc_pca = performance(algo, name, X_test, y_test, y_test_pred_pca)\n",
    "        auc.append(roc_auc_pca)\n",
    "    return auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "######################################## LogisticRegression ########################################\n",
      "Accuracy score for LogisticRegression : 0.8405635891731553\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.87      0.93      0.90     10192\n",
      "         1.0       0.72      0.57      0.64      3293\n",
      "\n",
      "    accuracy                           0.84     13485\n",
      "   macro avg       0.79      0.75      0.77     13485\n",
      "weighted avg       0.83      0.84      0.83     13485\n",
      "\n",
      "0.8952573986683136\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "######################################## DecisionTreeClassifier ########################################\n",
      "Accuracy score for DecisionTreeClassifier : 0.7994809047089358\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.86      0.88      0.87     10192\n",
      "         1.0       0.59      0.56      0.58      3293\n",
      "\n",
      "    accuracy                           0.80     13485\n",
      "   macro avg       0.73      0.72      0.72     13485\n",
      "weighted avg       0.80      0.80      0.80     13485\n",
      "\n",
      "0.7467020244407885\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "######################################## SVC ########################################\n",
      "Accuracy score for SVC : 0.8491657397107898\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.87      0.95      0.90     10192\n",
      "         1.0       0.77      0.55      0.64      3293\n",
      "\n",
      "    accuracy                           0.85     13485\n",
      "   macro avg       0.82      0.75      0.77     13485\n",
      "weighted avg       0.84      0.85      0.84     13485\n",
      "\n",
      "0.8839613910340235\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2min 37s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "auc = main(models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ROC AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.895257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DecisionTreeClassifier</th>\n",
       "      <td>0.746702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVC</th>\n",
       "      <td>0.883961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         ROC AUC\n",
       "LogisticRegression      0.895257\n",
       "DecisionTreeClassifier  0.746702\n",
       "SVC                     0.883961"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_comp_df = pd.DataFrame(auc, columns = ['ROC AUC'], index = classifiers)\n",
    "\n",
    "model_comp_df"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
